{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "   #  Spatial Declustering in Python for Engineers and Geoscientists \n",
    "\n",
    "## with GSLIB's DECLUS Program Converted to Python\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "\n",
    "#### Contacts: [Twitter/@GeostatsGuy](https://twitter.com/geostatsguy) | [GitHub/GeostatsGuy](https://github.com/GeostatsGuy) | [www.michaelpyrcz.com](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446)\n",
    "\n",
    "This is a tutorial for / demonstration of **spatial declustering in Python with GSLIB's DECLUS program translated to Python, wrappers and reimplementations of other GSLIB: Geostatistical Library methods** (Deutsch and Journel, 1997). Almost every spatial dataset is based on biased sampling.  This includes clustering (increased density of samples) over specific ranges of values.  For example, more samples in an area of high feature values.  Spatial declustering is a process of assigning data weights based on local data density.  The cell-based declustering approach (Deutsch and Journel, 1997; Pyrcz and Deutsch, 2014; Pyrcz and Deutsch, 2003, paper is available here: http://gaa.org.au/pdf/DeclusterDebias-CCG.pdf) is based on the use of a mesh over the area of interest.  Each datum's weight is inverse to the number of data in each cell.  Cell offsets of applied to smooth out influence of mesh origin.  Multiple cell sizes are applied and typically the cell size that minimizes the declustered distribution mean is applied for preferential sampling in the high-valued locations (the maximizing cell size is applied if the data is preferential sampled in the low-valued locations).  If there is a nominal data spacing with local clusters, then this spacing is the best cell size.\n",
    "\n",
    "This exercise demonstrates the cell-based declustering approach in Python with wrappers and reimplimentation of GSLIB methods.  The steps include:\n",
    "\n",
    "1. generate a 2D sequential Guassian simulation using a wrapper of GSLIB's sgsim method\n",
    "2. apply regular sampling to the 2D realization\n",
    "3. preferentially removing samples in the low-valued locations\n",
    "4. calculate cell-based declustering weights with the **declus function**\n",
    "5. visualize the location map of the declustering weights and the original exhaustive, sample and the new declustered distribution along with the scatter plot of declustered weight vs. cell size.\n",
    "\n",
    "To accomplish this I have provide wrappers or reimplementation in Python for the following GSLIB methods:\n",
    "\n",
    "1. sgsim - sequantial Gaussian simulation limited to 2D and unconditional\n",
    "2. hist - histograms plots reimplemented with GSLIB parameters using python methods\n",
    "3. locmap - location maps reimplemented with GSLIB parameters using python methods\n",
    "4. pixelplt - pixel plots reimplemented with GSLIB parameters using python methods\n",
    "5. locpix - my modification of GSLIB to superimpose a location map on a pixel plot reimplemented with GSLIB parameters using Python methods\n",
    "5. affine - affine correction adjust the mean and standard deviation of a feature reimplemented with GSLIB parameters using Python methods\n",
    "\n",
    "These methods are all in the functions declared upfront. To run this demo all one has to do is download and place in your working directory the following executables from the GSLIB/bin directory:\n",
    "\n",
    "1. sgsim.exe\n",
    "2. nscore.exe (not currently used in demo, but wrapper is included)\n",
    "\n",
    "The GSLIB source and executables are available at http://www.statios.com/Quick/gslib.html.  For the reference on using GSLIB check out the User Guide, GSLIB: Geostatistical Software Library and User's Guide by Clayton V. Deutsch and Andre G. Journel.\n",
    "\n",
    "I did this to allow people to use these GSLIB functions that are extremely robust in Python. Also this should be a bridge to allow so many familar with GSLIB to work in Python as a kept the parameterization and displays consistent with GSLIB.  The wrappers are simple functions declared below that write the parameter files, run the GSLIB executable in the working directory and load and visualize the output in Python. This will be included on GitHub for anyone to try it out https://github.com/GeostatsGuy/.  \n",
    "\n",
    "This was my first effort to translate the GSLIB Fortran to Python.  It was pretty easy so I'll start translating other critical GSLIB functions.\n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                 # to set current working directory \n",
    "import numpy as np                                        # arrays and matrix math\n",
    "import pandas as pd                                       # DataFrames\n",
    "import matplotlib.pyplot as plt                           # plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Declare functions\n",
    "\n",
    "Here are the wrappers and reimplementations of GSLIB method along with two utilities to load GSLIB's Geo-EAS from data files into DataFrames and 2D Numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some GeostatsPy Functions - by Michael Pyrcz, maintained at https://git.io/fNgR7.\n",
    "# A set of functions to provide access to GSLIB in Python.\n",
    "# GSLIB executables: nscore.exe, declus.exe, gam.exe, gamv.exe, vmodel.exe, kb2d.exe & sgsim.exe must be in the working directory \n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt                          \n",
    "import random as rand\n",
    "image_type = 'tif'; dpi = 600\n",
    "\n",
    "# utility to convert GSLIB Geo-EAS files to a 1D or 2D numpy ndarray for use with Python methods\n",
    "def GSLIB2ndarray(data_file,kcol,nx,ny): \n",
    "    colArray = []\n",
    "    if ny > 1:\n",
    "        array = np.ndarray(shape=(ny,nx),dtype=float,order='F')\n",
    "    else:\n",
    "        array = np.zeros(nx)    \n",
    "    with open(data_file) as myfile:   # read first two lines\n",
    "        head = [next(myfile) for x in range(2)]\n",
    "        line2 = head[1].split()\n",
    "        ncol = int(line2[0])          # get the number of columns\n",
    "        for icol in range(0, ncol):   # read over the column names\n",
    "            head = [next(myfile) for x in range(1)]\n",
    "            if icol == kcol:\n",
    "                col_name = head[0].split()[0]       \n",
    "        if ny > 1:\n",
    "            for iy in range(0,ny):\n",
    "                for ix in range(0,nx):\n",
    "                    head = [next(myfile) for x in range(1)]\n",
    "                    array[ny-1-iy][ix] = head[0].split()[kcol]\n",
    "        else:\n",
    "            for ix in range(0,nx):\n",
    "                head = [next(myfile) for x in range(1)]\n",
    "                array[ix] = head[0].split()[kcol]\n",
    "    return array,col_name \n",
    "\n",
    "# utility to convert GSLIB Geo-EAS files to a pandas DataFrame for use with Python methods\n",
    "def GSLIB2Dataframe(data_file):\n",
    "    colArray = []\n",
    "    with open(data_file) as myfile:   # read first two lines\n",
    "        head = [next(myfile) for x in range(2)]\n",
    "        line2 = head[1].split()\n",
    "        ncol = int(line2[0])\n",
    "        for icol in range(0, ncol):\n",
    "            head = [next(myfile) for x in range(1)]\n",
    "            colArray.append(head[0].split()[0])\n",
    "        data = np.loadtxt(myfile, skiprows = 0)\n",
    "        df = pd.DataFrame(data)\n",
    "        df.columns = colArray\n",
    "        return df\n",
    "\n",
    "# histogram, reimplemented in Python of GSLIB hist with MatPlotLib methods, displayed and as image file\n",
    "def hist(array,xmin,xmax,log,cumul,bins,weights,xlabel,title,fig_name):\n",
    "    plt.figure(figsize=(8,6))\n",
    "    cs = plt.hist(array, alpha = 0.2, color = 'red', edgecolor = 'black', bins=bins, range = [xmin,xmax], weights = weights, log = log, cumulative = cumul)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency')  \n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return\n",
    "\n",
    "# histogram, reimplemented in Python of GSLIB hist with MatPlotLib methods (version for subplots)\n",
    "def hist_st(array,xmin,xmax,log,cumul,bins,weights,xlabel,title):  \n",
    "    cs = plt.hist(array, alpha = 0.2, color = 'red', edgecolor = 'black', bins=bins, range = [xmin,xmax], weights = weights, log = log, cumulative = cumul)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency') \n",
    "    return\n",
    "\n",
    "# location map, reimplemention in Python of GSLIB locmap with MatPlotLib methods\n",
    "def locmap(df,xcol,ycol,vcol,xmin,xmax,ymin,ymax,vmin,vmax,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    ixy = 0 \n",
    "    plt.figure(figsize=(8,6))    \n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, norm=None, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)    \n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "# location map, reimplemention in Python of GSLIB locmap with MatPlotLib methods (version for subplots)\n",
    "def locmap_st(df,xcol,ycol,vcol,xmin,xmax,ymin,ymax,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n",
    "    ixy = 0   \n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, norm=None, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)    \n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    return im           \n",
    "\n",
    "# pixel plot, reimplemention in Python of GSLIB pixelplt with MatPlotLib methods\n",
    "def pixelplt(array,xmin,xmax,ymin,ymax,step,vmin,vmax,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    print(str(step))\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    plt.figure(figsize=(8,6))\n",
    "    im = plt.contourf(xx,yy,array,cmap=cmap,vmin=vmin,vmax=vmax,levels=np.linspace(vmin,vmax,100))\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im,orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "# pixel plot, reimplemention in Python of GSLIB pixelplt with MatPlotLib methods(version for subplots)\n",
    "def pixelplt_st(array,xmin,xmax,ymin,ymax,step,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    x = [];y = []; v = [] # use dummy since scatter plot controls legend min and max appropriately and contour does not!\n",
    "    cs = plt.contourf(xx,yy,array,cmap=cmap,vmin=vmin,vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(x,y,s=None, c=v, marker=None,cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.clim(vmin,vmax)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    return cs\n",
    "\n",
    "# pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods\n",
    "def locpix(array,xmin,xmax,ymin,ymax,step,vmin,vmax,df,xcol,ycol,vcol,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    plt.figure(figsize=(8,6))\n",
    "    cs = plt.contourf(xx, yy, array, cmap=cmap,vmin=vmin, vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)  \n",
    "    cbar = plt.colorbar(orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return cs\n",
    "\n",
    "# pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods(version for subplots)\n",
    "def locpix_st(array,xmin,xmax,ymin,ymax,step,vmin,vmax,df,xcol,ycol,vcol,title,xlabel,ylabel,vlabel,cmap):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    cs = plt.contourf(xx, yy, array, cmap=cmap,vmin=vmin, vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)  \n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)\n",
    "    cbar = plt.colorbar(orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "\n",
    "# affine distribution correction reimplemented in Python with numpy methods \n",
    "def affine(array,tmean,tstdev):    \n",
    "    if array.ndim != 2:\n",
    "        Print(\"Error: must use a 2D array\")\n",
    "        return\n",
    "    nx = array.shape[0]\n",
    "    ny = array.shape[1]\n",
    "    mean = np.average(array)\n",
    "    stdev = np.std(array)\n",
    "    for iy in range(0,ny):\n",
    "        for ix in range(0,nx):\n",
    "             array[ix,iy]= (tstdev/stdev)*(array[ix,iy] - mean) + tmean  \n",
    "    return(array)   \n",
    "\n",
    "def make_variogram(nug,nst,it1,cc1,azi1,hmaj1,hmin1,it2=1,cc2=0,azi2=0,hmaj2=0,hmin2=0):\n",
    "    if cc2 == 0:\n",
    "        nst = 1\n",
    "    var = dict([('nug', nug), ('nst', nst), ('it1', it1),('cc1', cc1),('azi1', azi1),('hmaj1', hmaj1), ('hmin1', hmin1), \n",
    "      ('it2', it2),('cc2', cc2),('azi2', azi2),('hmaj2', hmaj2), ('hmin2', hmin2)])\n",
    "    if nug + cc1 + cc2 != 1:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: sill does not sum to 1.0, do not use in simulation \\x1b[0m')\n",
    "    if cc1 < 0 or cc2 < 0 or nug < 0 or hmaj1 < 0 or hmaj2 < 0 or hmin1 < 0 or hmin2 < 0:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: contributions and ranges must be all positive \\x1b[0m')\n",
    "    if hmaj1 < hmin1 or hmaj2 < hmin2:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: major range should be greater than minor range \\x1b[0m')\n",
    "    return var\n",
    "    \n",
    "# sequential Gaussian simulation, 2D unconditional wrapper for sgsim from GSLIB (.exe must be in working directory)\n",
    "def GSLIB_sgsim_2d_uncond(nreal,nx,ny,hsiz,seed,var,output_file):\n",
    "    import os\n",
    "    import numpy as np \n",
    "    \n",
    "    nug = var['nug']\n",
    "    nst = var['nst']; it1 = var['it1']; cc1 = var['cc1']; azi1 = var['azi1']; hmaj1 = var['hmaj1']; hmin1 = var['hmin1'] \n",
    "    it2 = var['it2']; cc2 = var['cc2']; azi2 = var['azi2']; hmaj2 = var['hmaj2']; hmin2 = var['hmin2']     \n",
    "    max_range = max(hmaj1,hmaj2) \n",
    "    hmn = hsiz * 0.5   \n",
    "    hctab = int(max_range/hsiz)*2 + 1\n",
    "    \n",
    "    sim_array = np.random.rand(nx,ny)\n",
    "  \n",
    "    file = open(\"sgsim.par\", \"w\")\n",
    "    file.write(\"              Parameters for SGSIM                                         \\n\")\n",
    "    file.write(\"              ********************                                         \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETER:                                                        \\n\")\n",
    "    file.write(\"none                          -file with data                              \\n\")\n",
    "    file.write(\"1  2  0  3  5  0              -  columns for X,Y,Z,vr,wt,sec.var.          \\n\")\n",
    "    file.write(\"-1.0e21 1.0e21                -  trimming limits                           \\n\")\n",
    "    file.write(\"0                             -transform the data (0=no, 1=yes)            \\n\")\n",
    "    file.write(\"none.trn                      -  file for output trans table               \\n\")\n",
    "    file.write(\"1                             -  consider ref. dist (0=no, 1=yes)          \\n\")\n",
    "    file.write(\"none.dat                      -  file with ref. dist distribution          \\n\")\n",
    "    file.write(\"1  0                          -  columns for vr and wt                     \\n\")\n",
    "    file.write(\"-4.0    4.0                   -  zmin,zmax(tail extrapolation)             \\n\")\n",
    "    file.write(\"1      -4.0                   -  lower tail option, parameter              \\n\")\n",
    "    file.write(\"1       4.0                   -  upper tail option, parameter              \\n\")\n",
    "    file.write(\"0                             -debugging level: 0,1,2,3                    \\n\")\n",
    "    file.write(\"nonw.dbg                      -file for debugging output                   \\n\")\n",
    "    file.write(str(output_file) + \"           -file for simulation output                  \\n\")\n",
    "    file.write(str(nreal) + \"                 -number of realizations to generate          \\n\")\n",
    "    file.write(str(nx) + \" \" + str(hmn) + \" \" + str(hsiz) + \"                              \\n\")\n",
    "    file.write(str(ny) + \" \" + str(hmn) + \" \" + str(hsiz) + \"                              \\n\")\n",
    "    file.write(\"1 0.0 1.0                     - nz zmn zsiz                                \\n\")\n",
    "    file.write(str(seed) + \"                  -random number seed                          \\n\")\n",
    "    file.write(\"0     8                       -min and max original data for sim           \\n\")\n",
    "    file.write(\"12                            -number of simulated nodes to use            \\n\")\n",
    "    file.write(\"0                             -assign data to nodes (0=no, 1=yes)          \\n\")\n",
    "    file.write(\"1     3                       -multiple grid search (0=no, 1=yes),num      \\n\")\n",
    "    file.write(\"0                             -maximum data per octant (0=not used)        \\n\")\n",
    "    file.write(str(max_range) + \" \" + str(max_range) + \" 1.0 -maximum search  (hmax,hmin,vert) \\n\")\n",
    "    file.write(str(azi1) + \"   0.0   0.0       -angles for search ellipsoid                 \\n\")\n",
    "    file.write(str(hctab) + \" \" + str(hctab) + \" 1 -size of covariance lookup table        \\n\")\n",
    "    file.write(\"0     0.60   1.0              -ktype: 0=SK,1=OK,2=LVM,3=EXDR,4=COLC        \\n\")\n",
    "    file.write(\"none.dat                      -  file with LVM, EXDR, or COLC variable     \\n\")\n",
    "    file.write(\"4                             -  column for secondary variable             \\n\")\n",
    "    file.write(str(nst) + \" \" + str(nug) + \"  -nst, nugget effect                          \\n\")\n",
    "    file.write(str(it1) + \" \" + str(cc1) + \" \" +str(azi1) + \" 0.0 0.0 -it,cc,ang1,ang2,ang3\\n\")\n",
    "    file.write(\" \" + str(hmaj1) + \" \" + str(hmin1) + \" 1.0 - a_hmax, a_hmin, a_vert        \\n\")\n",
    "    file.write(str(it2) + \" \" + str(cc2) + \" \" +str(azi2) + \" 0.0 0.0 -it,cc,ang1,ang2,ang3\\n\")\n",
    "    file.write(\" \" + str(hmaj2) + \" \" + str(hmin2) + \" 1.0 - a_hmax, a_hmin, a_vert        \\n\")  \n",
    "    file.close()\n",
    "\n",
    "    os.system('\"sgsim.exe sgsim.par\"')       \n",
    "    sim_array = GSLIB2ndarray(output_file,0,nx,ny)         \n",
    "    return(sim_array[0])\n",
    "\n",
    "# extract regular spaced samples from a model   \n",
    "def regular_sample(array,xmin,xmax,ymin,ymax,step,mx,my,name):\n",
    "    x = []; y = []; v = []; iix = 0; iiy = 0;\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    iiy = 0\n",
    "    for iy in range(0,ny):\n",
    "        if iiy >= my:\n",
    "            iix = 0\n",
    "            for ix in range(0,nx):\n",
    "                if iix >= mx:\n",
    "                    x.append(xx[ix,iy]);y.append(yy[ix,iy]); v.append(array[ix,iy])\n",
    "                    iix = 0; iiy = 0\n",
    "                iix = iix + 1\n",
    "        iiy = iiy + 1\n",
    "    df = pd.DataFrame(np.c_[x,y,v],columns=['X', 'Y', name])\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the translation of declus to Python (Michael Pyrcz, Jan. 2019 - let me know if you find any issues)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# GSLIB's DECLUS program (Deutsch and Journel, 1998) converted from the original Fortran to Python \n",
    "# by Michael Pyrcz, the University of Texas at Austin (Jan, 2019)\n",
    "# note this was simplified to 2D only\n",
    "def declus(df,xcol,ycol,vcol,iminmax,noff,ncell,cmin,cmax):\n",
    "# Parameters - consistent with original GSLIB    \n",
    "# df - Pandas DataFrame with the spatial data\n",
    "# xcol, ycol - name of the x and y coordinate columns\n",
    "# vcol - name of the property column\n",
    "# iminmax - 1 / True for use cell size with max decluster mean, 0 / False for declustered mean minimizing cell size\n",
    "# noff - number of offsets\n",
    "# ncell - number of cell sizes\n",
    "# cmin, cmax - min and max cell size\n",
    "#\n",
    "# Load Data and Set Up Arrays\n",
    "    nd = len(df)\n",
    "    x = df[xcol].values\n",
    "    y = df[ycol].values\n",
    "    v = df[vcol].values\n",
    "    wt = np.zeros(nd)\n",
    "    wtopt = np.ones(nd)\n",
    "    index = np.zeros(nd, np.int32)\n",
    "    xcs_mat = np.zeros(ncell+2) # we use 1,...,n for this array\n",
    "    vrcr_mat = np.zeros(ncell+2) # we use 1,...,n for this array\n",
    "    anisy = 1.0   # hard code the cells to 2D isotropic\n",
    "    roff = float(noff)\n",
    "    \n",
    "# Calculate extents    \n",
    "    xmin = np.min(x); xmax = np.max(x)\n",
    "    ymin = np.min(y); ymax = np.max(y)\n",
    "      \n",
    "# Calculate summary statistics\n",
    "    vmean = np.mean(v)\n",
    "    vstdev = np.std(v)\n",
    "    vmin = np.min(v)\n",
    "    vmax = np.max(v)\n",
    "    xcs_mat[0] = 0.0; vrcr_mat[0] = vmean; vrop = vmean # include the naive case\n",
    "    print('There are ' + str(nd) + ' data with:')\n",
    "    print('   mean of      ' + str(vmean) + ' ')\n",
    "    print('   min and max  ' + str(vmin) + ' and ' + str(vmax))\n",
    "    print('   standard dev ' + str(vstdev) + ' ')\n",
    "    \n",
    "# define a \"lower\" origin to use for the cell sizes:\n",
    "    xo1 = xmin - 0.01\n",
    "    yo1 = ymin - 0.01\n",
    "\n",
    "# define the increment for the cell size:\n",
    "    xinc = (cmax-cmin) / ncell\n",
    "    yinc = xinc\n",
    "\n",
    "# loop over \"ncell+1\" cell sizes in the grid network:\n",
    "    ncellx = int((xmax-(xo1-cmin))/cmin)+1\n",
    "    ncelly = int((ymax-(yo1-cmin*anisy))/(cmin))+1\n",
    "    ncellt = ncellx*ncelly \n",
    "    cellwt = np.zeros(ncellt)\n",
    "    xcs =  cmin - xinc\n",
    "    ycs = (cmin*anisy) - yinc\n",
    "\n",
    "# MAIN LOOP over cell sizes:\n",
    "    for lp in range(1,ncell+2):   # 0 index is the 0.0 cell, note n + 1 in Fortran\n",
    "        xcs = xcs + xinc\n",
    "        ycs = ycs + yinc\n",
    "        \n",
    "# initialize the weights to zero: \n",
    "        wt.fill(0.0)\n",
    "\n",
    "# determine the maximum number of grid cells in the network:\n",
    "        ncellx = int((xmax-(xo1-xcs))/xcs)+1\n",
    "        ncelly = int((ymax-(yo1-ycs))/ycs)+1\n",
    "        ncellt = float(ncellx*ncelly)\n",
    "\n",
    "# loop over all the origin offsets selected:\n",
    "        xfac = min((xcs/roff),(0.5*(xmax-xmin)))\n",
    "        yfac = min((ycs/roff),(0.5*(ymax-ymin)))\n",
    "        for kp in range(1,noff+1):\n",
    "            xo = xo1 - (float(kp)-1.0)*xfac\n",
    "            yo = yo1 - (float(kp)-1.0)*yfac\n",
    "\n",
    "# initialize the cumulative weight indicators:\n",
    "            cellwt.fill(0.0)\n",
    "    \n",
    "# determine which cell each datum is in:\n",
    "            for i in range(0,nd):\n",
    "                icellx = int((x[i] - xo)/xcs) + 1\n",
    "                icelly = int((y[i] - yo)/ycs) + 1\n",
    "                icell  = icellx + (icelly-1)*ncellx  \n",
    "                index[i] = icell\n",
    "                cellwt[icell] = cellwt[icell] + 1.0\n",
    "\n",
    "\n",
    "# The weight assigned to each datum is inversely proportional to the\n",
    "# number of data in the cell.  We first need to get the sum of weights\n",
    "# so that we can normalize the weights to sum to one:\n",
    "            sumw = 0.0\n",
    "            for i in range(0,nd):\n",
    "                ipoint = index[i]\n",
    "                sumw   = sumw + (1.0 / cellwt[ipoint])\n",
    "            sumw = 1.0 / sumw\n",
    "                \n",
    "# Accumulate the array of weights (that now sum to one):\n",
    "            for i in range(0,nd):\n",
    "                ipoint = index[i]\n",
    "                wt[i] = wt[i] + (1.0/cellwt[ipoint])*sumw\n",
    "\n",
    "# End loop over all offsets:\n",
    "\n",
    "# compute the weighted average for this cell size:\n",
    "        sumw  = 0.0\n",
    "        sumwg = 0.0\n",
    "        for i in range(0,nd):\n",
    "            sumw  = sumw + wt[i]\n",
    "            sumwg = sumwg + wt[i]*v[i]\n",
    "        vrcr = sumwg / sumw\n",
    "        vrcr_mat[lp] = vrcr\n",
    "        xcs_mat[lp] = xcs\n",
    "\n",
    "# see if this weighting is optimal:\n",
    "        if iminmax and vrcr < vrop or not iminmax and vrcr > vrop or ncell == 1:\n",
    "            best = xcs\n",
    "            vrop = vrcr\n",
    "            wtopt = wt.copy()   # deep copy\n",
    "\n",
    "# END MAIN LOOP over all cell sizes:\n",
    "\n",
    "# Get the optimal weights:\n",
    "    sumw = 0.0\n",
    "    for i in range(0,nd):\n",
    "        sumw = sumw + wtopt[i]\n",
    "    wtmin = np.min(wtopt)\n",
    "    wtmax = np.max(wtopt)\n",
    "    facto = float(nd) / sumw\n",
    "    wtopt = wtopt * facto\n",
    "    return wtopt,xcs_mat,vrcr_mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE337/DataAnalysis\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will have to update the part in quotes with your own working directory and the format is different on a Mac (e.g. \"~/PGE\"). \n",
    "\n",
    "##### Make a 2D spatial model\n",
    "\n",
    "The following are the basic parameters for the demonstration.  This includes the number of cells in the 2D regular grid, the cell size (step) and the x and y min and max along with the color scheme.\n",
    "\n",
    "Then we make a single realization of a Gausian distributed feature over the specified 2D grid and then apply affine correction to ensure we have a reasonable mean and spread for our feature's distribution, assumed to be Porosity (e.g. no negative values) while retaining the Gaussian distribution.  Any transform could be applied at this point.  We are keeping this workflow simple. *This is our truth model that we will sample*.\n",
    "\n",
    "The parameters of *GSLIB_sgsim_2d_uncond* are (nreal,nx,ny,hsiz,seed,hrange1,hrange2,azi,output_file).  nreal is the number of realizations, nx and ny are the number of cells in x and y, hsiz is the cell siz, seed is the random number seed, hrange and hrange2 are the variogram ranges in major and minor directions respectively, azi is the azimuth of the primary direction of continuity (0 is aligned with Y axis) and output_file is a GEO_DAS file with the simulated realization.  The ouput is the 2D numpy array of the simulation along with the name of the property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x27e318233c8>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx = 100; ny = 100; cell_size = 10                               # grid number of cells and cell size\n",
    "xmin = 0.0; ymin = 0.0;                                          # grid origin\n",
    "xmax = xmin + nx * cell_size; ymax = ymin + ny * cell_size       # calculate the extent of model\n",
    "seed = 74073                                                     # random number seed  for stochastic simulation    \n",
    "range_max = 1800; range_min = 500; azimuth = 65                  # Porosity variogram ranges and azimuth\n",
    "vario = make_variogram(0.0,nst=1,it1=1,cc1=1.0,azi1=65,hmaj1=1800,hmin1=500)\n",
    "mean = 10.0; stdev = 2.0                                         # Porosity mean and standard deviation\n",
    "#cmap = plt.cm.RdYlBu\n",
    "vmin = 4; vmax = 16; cmap = plt.cm.plasma                        # color min and max and using the plasma color map\n",
    "\n",
    "# calculate a stochastic realization with standard normal distribution\n",
    "sim = GSLIB_sgsim_2d_uncond(1,nx,ny,cell_size,seed,vario,\"simulation\")\n",
    "sim = affine(sim,mean,stdev)                                     # correct the distribution to a target mean and standard deviation.\n",
    "sampling_ncell = 10                                              # sample every 10th node from the model\n",
    "samples = regular_sample(sim,xmin,xmax,ymin,ymax,sampling_ncell,10,10,'Realization')\n",
    "samples_cluster = samples.drop([80,79,78,73,72,71,70,65,64,63,61,57,56,54,53,47,45,42]) # this removes specific rows (samples)\n",
    "samples_cluster = samples_cluster.reset_index(drop=True)         # we reset and remove the index (it is not sequential anymore)\n",
    "locpix(sim,xmin,xmax,ymin,ymax,cell_size,vmin,vmax,samples_cluster,'X','Y','Realization','Porosity Realization and Regular Samples','X(m)','Y(m)','Porosity (%)',cmap,\"Por_Samples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the distribution and means of the truth model and the spatially clustered samples.  We do this with the hist function that is reimplemented from GSLIB's hist method for histogram plotting.  The parameters of hist are (array,xmin,xmax,log,cumul,bins,weights,xlabel,title), including array, xmin and xmax the data array and minimum and maximum of the feature, log and cumul with true for log axis and cumulative distribution function, bins for the number of bins, weights for an array of same size of the data array with weights and the remainder are labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pq2YAAAAAgJWmFqB29xOOMvzSY8x/QZIXHGX87Unevo6lAQAAAACsyczvgQoAAAAAsFkIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAAAAARghQAQAAAABGCFABAAAAAEYIUAEAYIOrqv9YVR+uqg9V1Wuq6rbzrgkAYLsQoAIAwAZWVacmeVqS3d39gCQnJnn8fKsCANg+BKgAALDx7Uhyu6rakeTkJNfOuR4AgG1DgAoAABtYd388ya8muTrJdUk+3d3vmG9VAADbhwAVAAA2sKq6c5Jzk9wnyb2S3L6qfmjVnAuqal9V7VtaWppHmQAAW5YAFQAANraHJ7mqu5e6+4tJ3pzkW1dO6O6Lunt3d+9eWFiYS5EAAFuVABUAADa2q5PsqaqTq6qSnJNkcc41AQBsGwJUAADYwLr7vUnemOT9SfZn0sNfNNeiAAC2kR3zLgAAADi27n5ukufOuw4AgO3IGagAAAAAACMEqAAAAAAAI6YWoFbVy6rq+qr60Iqxu1TVpVV1YPh952G8qurXq+qKqvrbqjprxXvOH+YfqKrzp1UvAAAAAMBq0zwD9feTPGLV2IVJ3tXdZyZ51/A6SR6Z5Mzh54Ikv5VMAtdM7vX0kCQPTvLcI6ErAAAAAMC0TS1A7e53J7lh1fC5SS4eti9Oct6K8Vf0xN4kO6vqnkn+XZJLu/uG7v5Ukktzy1AWAAAAAGAqZn0P1Ht093VJMvy++zB+apJrVsw7OIyNjQMAAAAATN1GeYhUHWWsjzF+ywNUXVBV+6pq39LS0roWBwAAAABsT7MOUD8xXJqf4ff1w/jBJKevmHdakmuPMX4L3X1Rd+/u7t0LCwvrXjgAAAAAsP3smPF6lyQ5P8kLh99vXTH+U1X12kweGPXp7r6uqv4syS+teHDUdyd59oxrBgb79+7N4eXlqa9z1eJizvZFCAAAALABTC1ArarXJHlokrtV1cEkz80kOH19VT0lydVJHjdMf3uSRyW5Islnkzw5Sbr7hqr6L0neN8z7xe5e/WAqYEYOLy/PJNg8sG/f1NcAAAAAWIupBajd/YSRXeccZW4neerIcV6W5GXrWBoAAAAAwJpslIdIAQAAAABsOAJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAETvmXQAAAAAwe/v37s3h5eWZrHXV4mLOXliYyVoA602ACgAAANvQ4eXlmYWaB/btm8k6ANPgEn4AAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAETvmXQAA6+/KxcWZrXXSzp154J49M1sPAAAAZkmACrAF3XzoUM5eWJjJWpcvLc1kHQAAAJgHl/ADAAAAAIwQoAIAAAAAjBCgAgAAAACMEKACAAAAAIwQoAIAAAAAjBCgAgAAAACMEKACAAAAAIwQoAIAAAAAjBCgAgAAAACMmEuAWlX/sao+XFUfqqrXVNVtq+o+VfXeqjpQVa+rqpOGubcZXl8x7D9jHjUDAAAAANvPzAPUqjo1ydOS7O7uByQ5Mcnjk/xykhd195lJPpXkKcNbnpLkU939dUleNMwDAAAAAJi6eV3CvyPJ7apqR5KTk1yX5GFJ3jjsvzjJecP2ucPrDPvPqaqaYa0AAAAAwDY18wC1uz+e5FeTXJ1JcPrpJJcnWe7um4ZpB5OcOmyfmuSa4b03DfPvOsuaAQAAAIDtaR6X8N85k7NK75PkXklun+SRR5naR95yjH0rj3tBVe2rqn1LS0vrVS4AAAAAsI3N4xL+hye5qruXuvuLSd6c5FuT7Bwu6U+S05JcO2wfTHJ6kgz775TkhtUH7e6Lunt3d+9eWFiY9mcAAAAAALaBeQSoVyfZU1UnD/cyPSfJR5L8RZLHDnPOT/LWYfuS4XWG/X/e3bc4AxUAAAAAYL3N4x6o783kYVDvT7J/qOGiJM9K8oyquiKTe5y+dHjLS5PcdRh/RpILZ10zAADMU1XtrKo3VtXfVdViVX3LvGsCANgudhx/yvrr7ucmee6q4Y8mefBR5n4+yeNmURcAAGxQL0nyp9392Ko6KcnJ8y4IAGC7mEuACgAArE1V3THJdyT5kSTp7sNJDs+zJgCA7WQe90AFAADW7r5JlpK8vKo+UFW/V1W3n3dRAADbhTNQAQBgY9uR5KwkP93d762ql2TyXID/dGRCVV2Q5IIk2bVr11yKBIBba//evTm8vDyTtU7auTMP3LNnJmux9QhQAQBgYzuY5ODwMNZk8kDWf/Fg1e6+KJMHs2b37t092/IA4CtzeHk5Zy8szGSty5eWZrIOW5NL+AEAYAPr7n9Mck1V3W8YOifJR+ZYEgDAtuIMVAAA2Ph+Osmrq+qkJB9N8uQ51wMAsG0IUAEAYIPr7g8m2T3vOgAAtiOX8AMAAAAAjBCgAgAAAACMWNMl/FX1gO7+0LSLAW69/Xv35vDy8kzWumpxcWZPSASAzUz/DACwdaz1Hqi/Pdyw/veT/EF3zyatAY7r8PLyzELNA/v2zWQdANgC9M8AAFvEmi7h7+5/k+SJSU5Psq+q/qCqvmuqlQEAwCalfwYA2DrWfA/U7j6Q5OeTPCvJdyb59ar6u6r699MqDgAANiv9MwDA1rCmALWqvrGqXpRkMcnDknxvd3/9sP2iKdYHAACbjv4ZAGDrWOs9UP9bkt9N8pzu/tyRwe6+tqp+fiqVAQDA5qV/BgDYItYaoD4qyee6+0tJUlUnJLltd3+2u185teoAAGBz0j8DAGwRa70H6juT3G7F65OHMQAA4Jb0zwAAW8RaA9Tbdvc/H3kxbJ88nZIAAGDT0z8DAGwRaw1QD1XVWUdeVNXZST53jPkAALCd6Z8BALaItd4D9WeSvKGqrh1e3zPJD0ynJAAA2PT0zwAAW8SaAtTufl9V/esk90tSSf6uu7841coAAGCT0j8DAGwdaz0DNUm+OckZw3seVFXp7ldMpSoAANj89M8AAFvAmgLUqnplkq9N8sEkXxqGO4kGEAAAVtE/AwBsHWs9A3V3kvt3d0+zGAAA2CL0zwAAW8QJa5z3oST/apqFAADAFqJ/BgDYItZ6Burdknykqv4myReODHb3Y6ZSFQAAbG76ZwCALWKtAerzplkEAABsMc+bdwEAAKyPNQWo3f2XVXXvJGd29zur6uQkJ063NAAA2Jz0zwAAW8ea7oFaVT+W5I1JfmcYOjXJW6ZVFAAAbGb6ZwCArWOtD5F6apJvS3JjknT3gSR3n1ZRAACwyemfAQC2iLUGqF/o7sNHXlTVjiQ9nZIAAGDT0z8DAGwRaw1Q/7KqnpPkdlX1XUnekOSPplcWAABsavpnAIAtYq0B6oVJlpLsT/LjSd6e5OenVRQAAGxy+mcAgC1ix1omdffNSX53+AEAAI5B/wwAsHWsKUCtqqtylHs2dfd9170iAADY5PTPAABbx5oC1CS7V2zfNsnjktxl/csBAIAtQf8MALBFrOkeqN39yRU/H+/uFyd52JRrAwCATUn/DACwdaz1Ev6zVrw8IZNv1E+ZSkUAALDJ6Z8BALaOtV7C/19XbN+U5GNJvn/dqwEAgK1B/wwAsEWsKUDt7n877UIAAGCr0D8DAGwda72E/xnH2t/dv7Y+5QAAwOanfwYA2DrWegn/7iTfnOSS4fX3Jnl3kmumURQAAGxy+mcAgC1irQHq3ZKc1d2fSZKqel6SN3T3/zmtwgAAYBPTPwMAbBFrDVB3JTm84vXhJGesezUAALA16J9hBvbv3ZvDy8szWeuknTvzwD17ZrIWm8OVi4szW8u/fzBfaw1QX5nkb6rqD5N0ku9L8oqpVQUAAJub/hlm4PDycs5eWJjJWpcvLc1kHTaPmw8d8u8fbBNrClC7+wVV9SdJvn0YenJ3f2B6ZQEAwOalfwYA2DpOuBVzT05yY3e/JMnBqrrPlGoCAICtQP8MALAFrClArarnJnlWkmcPQ1+T5FXTKgoAADYz/TMAwNax1jNQvy/JY5IcSpLuvjbJKdMqCgAANjn9MwDAFrHWAPVwd3cmN8BPVd1+eiUBAMCmp38GANgi1hqgvr6qfifJzqr6sSTvTPK70ysLAAA2Nf0zAMAWsWMtk7r7V6vqu5LcmOR+SX6huy+damUAALBJ6Z8BALaO4waoVXVikj/r7ocn0fQBAMAx6J8BALaW417C391fSvLZqrrTDOoBAIBNTf8MALC1rOkS/iSfT7K/qi7N8CTRJOnup02lKgAA2Nz0zwAAW8RaA9Q/Hn7WRVXtTPJ7SR6QyZNJfzTJ3yd5XZIzknwsyfd396eqqpK8JMmjknw2yY909/vXqxYAAJiCde2fAQCYn2MGqFW1q7uv7u6L13ndlyT50+5+bFWdlOTkJM9J8q7ufmFVXZjkwiTPSvLIJGcOPw9J8lvDbwAA2FCm2D8DADAnx7sH6luObFTVm9Zjwaq6Y5LvSPLSJOnuw929nOTcJEcazYuTnDdsn5vkFT2xN8nOqrrnetQCAADrbN37ZwAA5ut4AWqt2L7vOq153yRLSV5eVR+oqt+rqtsnuUd3X5ckw++7D/NPTXLNivcfHMb+ZaFVF1TVvqrat7S0tE6lAgDArTKN/hkAgDk6XoDaI9tfjR1JzkryW939oExuqn/hMebXUcZuUUt3X9Tdu7t798LCwvpUCgAAt840+mcAAOboeA+R+qaqujGTEPN2w3aG193dd/wK1jyY5GB3v3d4/cZMAtRPVNU9u/u64RL961fMP33F+09Lcu1XsC4AAEzbNPpnAADm6JhnoHb3id19x+4+pbt3DNtHXn9FzV93/2OSa6rqfsPQOUk+kuSSJOcPY+cneeuwfUmSH66JPUk+feRSfwAA2Eim0T8DADBfxzsDdVp+Osmrq+qkJB9N8uRMwtzXV9VTklyd5HHD3LcneVSSK5J8dpgLAAAAADB1cwlQu/uDSXYfZdc5R5nbSZ469aIAAAAAAFaZ1xmoAGwRVy4uzmSdk3buzAP37JnJWgAAAHCEABWAr8rNhw7l7IWFqa9z+dLS1NcAAACA1Y75ECkAAAAAgO1MgAoAAAAAMEKACgAAAAAwQoAKAAAAADBCgAoAAAAAMEKACgAAAAAwQoAKAAAAADBCgAoAAAAAMEKACgAAAAAwQoAKAAAAADBCgAoAAAAAMEKACgAAAAAwQoAKAAAAADBCgAoAAAAAMEKACgAAAAAwYse8C4CtaP/evTm8vDyTta5aXMzZCwszWQsAmJ+qOjHJviQf7+5Hz7seAIDtQoAKU3B4eXlmoeaBfftmsg4AMHdPT7KY5I7zLgQAYDsRoALAKrM8i/yknTvzwD17ZrIWsHlV1WlJvifJC5I8Y87lAABsKwJUAFhllmeRX760NJN1gE3vxUl+NskpR9tZVRckuSBJdu3aNcOyYPubchyUAAAVA0lEQVS4cnFxJutcfc012XX66TNZy+3AANZGgAoAABtYVT06yfXdfXlVPfRoc7r7oiQXJcnu3bt7huXBtnHzoUMzCRsP7NuXs886a+rrHFkLgOM7Yd4FAAAAx/RtSR5TVR9L8tokD6uqV823JACA7UOACgAAG1h3P7u7T+vuM5I8Psmfd/cPzbksAIBtQ4AKAAAAADDCPVABAGCT6O7Lklw25zIAALYVASoAm8KsnnybeCItAAAAXyZABWBTmNWTbxNPpAUAAODL3AMVAAAAAGCEABUAAAAAYIQAFQAAAABghAAVAAAAAGCEABUAAAAAYIQAFQAAAABghAAVAAAAAGCEABUAAAAAYIQAFQAAAABghAAVAAAAAGCEABUAAAAAYIQAFQAAAABghAAVAAAAAGCEABUAAAAAYIQAFQAAAABghAAVAAAAAGCEABUAAAAAYIQAFQAAAABghAAVAAAAAGCEABUAAAAAYIQAFQAAAABghAAVAAAAAGCEABUAAAAAYIQAFQAAAABghAAVAAAAAGCEABUAAAAAYMTcAtSqOrGqPlBVbxte36eq3ltVB6rqdVV10jB+m+H1FcP+M+ZVMwAAAACwvczzDNSnJ1lc8fqXk7you89M8qkkTxnGn5LkU939dUleNMwDAAAAAJi6uQSoVXVaku9J8nvD60rysCRvHKZcnOS8Yfvc4XWG/ecM8wEAAAAApmrHnNZ9cZKfTXLK8PquSZa7+6bh9cEkpw7bpya5Jkm6+6aq+vQw/59mVy4ATMeVi4vHn7ROTtq5Mw/cs2dm6wEAAGwFMw9Qq+rRSa7v7sur6qFHho8ytdewb+VxL0hyQZLs2rVrHSoFgOm7+dChnL2wMJO1Ll9amsk6AAAAW8k8LuH/tiSPqaqPJXltJpfuvzjJzqo6EuieluTaYftgktOTZNh/pyQ3rD5od1/U3bu7e/fCjP5HFAAAAADY2mYeoHb3s7v7tO4+I8njk/x5dz8xyV8keeww7fwkbx22LxleZ9j/5919izNQAQAAAADW27zugXo0z0ry2qp6fpIPJHnpMP7SJK+sqisyOfP08XOqDwAAgOPYv3dvDi8vz2StqxYXZ3YrHNgutup/w7N69oDnDmxNcw1Qu/uyJJcN2x9N8uCjzPl8ksfNtDAAAAC+IoeXl2cWiBzYt28m68B2slX/G57Vswc8d2Brmsc9UAEAAAAANgUBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADAiB3zLgAAmI0rFxdnss5JO3fmgXv2zGQtAACAaROgAsA2cfOhQzl7YWHq61y+tDT1NQAAAGbFJfwAAAAAACMEqAAAAAAAIwSoAAAAAAAjBKgAAAAAACMEqAAAAAAAIwSoAAAAAAAjBKgAAAAAACMEqAAAAAAAI3bMuwCYlf179+bw8vJM1rpqcTFnLyzMZC0AAAAApkeAyrZxeHl5ZqHmgX37ZrIOAAAAANPlEn4AAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAAAACAEQJUAAAAAIARAlQAAAAAgBECVAAA2MCq6vSq+ouqWqyqD1fV0+ddEwDAdrJj3gUAAADHdFOSZ3b3+6vqlCSXV9Wl3f2ReRcGALAdOAMVAAA2sO6+rrvfP2x/JsliklPnWxUAwPYhQAUAgE2iqs5I8qAk751vJQAA24dL+AEAYBOoqjskeVOSn+nuG1ftuyDJBUmya9euOVQHwDRdubg4s7WuWlzM2QsLM1sPNgMBKgAAbHBV9TWZhKev7u43r97f3RcluShJdu/e3TMuD4Apu/nQoZmFmgf27ZvJOrCZuIQfAAA2sKqqJC9NstjdvzbvegAAthsBKgAAbGzfluRJSR5WVR8cfh4176IAALYLl/ADAMAG1t3vSVLzrgMAYLtyBioAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwIiZB6hVdXpV/UVVLVbVh6vq6cP4Xarq0qo6MPy+8zBeVfXrVXVFVf1tVZ0165oBAAAAgO1pHmeg3pTkmd399Un2JHlqVd0/yYVJ3tXdZyZ51/A6SR6Z5Mzh54IkvzX7kgEAAACA7WjmAWp3X9fd7x+2P5NkMcmpSc5NcvEw7eIk5w3b5yZ5RU/sTbKzqu4547IBAAAAgG1orvdAraozkjwoyXuT3KO7r0smIWuSuw/TTk1yzYq3HRzGVh/rgqraV1X7lpaWplk2AAAAALBNzC1Arao7JHlTkp/p7huPNfUoY32Lge6Lunt3d+9eWFhYrzIBAAAAgG1sLgFqVX1NJuHpq7v7zcPwJ45cmj/8vn4YP5jk9BVvPy3JtbOqFQAAAADYvmYeoFZVJXlpksXu/rUVuy5Jcv6wfX6St64Y/+Ga2JPk00cu9QcAAAAAmKYdc1jz25I8Kcn+qvrgMPacJC9M8vqqekqSq5M8btj39iSPSnJFks8mefJsywUAAAAAtquZB6jd/Z4c/b6mSXLOUeZ3kqdOtSgAAAAAgKOY20OkAAAAAAA2OgEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMCIHfMuAPbv3ZvDy8tTX+eqxcWcvbAw9XUAAAAA2DoEqMzd4eXlmQSbB/btm/oaAAAAAGwtLuEHAAAAABjhDFQAYF1dubg4s7VO2rkzD9yzZ2brAQAA248AFQBYVzcfOjSze05fvrQ0k3UAAIDtyyX8AAAAAAAjBKgAAAAAACMEqAAAAAAAIwSoAAAAAAAjBKgAAAAAACMEqAAAAAAAIwSoAAAAAAAjdsy7AAAAgO1o/969Oby8PJO1Ttq5Mw/cs2cmawHAViNABQAAmIPDy8s5e2FhJmtdvrQ0k3UAYCtyCT8AAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwIgd8y6AjWn/3r05vLw8k7WuWlzM2QsLM1kLAAAAAG4NASpHdXh5eWah5oF9+2ayDgAAAADcWi7hBwAAAAAYIUAFAAAAABghQAUAAAAAGCFABQAAAAAYIUAFAAAAABghQAUA4H+1d//BntV1HcefL3clWZJWY/2By4QUP0ZRCdAWS0uUkZIAZ6qRtGFGy2oKxSYTYuSPZmwoLTL7NYBwaSIcW9B1CAkCwprapWUFdvWKoAisUCwTi7gxLdu+++N7dvpyvV+Wu3vPOd/v9z4f/9zvOfd89/O+79n93td+zuecI0mSJGkEJ1AlSZIkSZIkaYTlfRcgSZK0r74xO9vZWAesXMlr1qzpbDxJkiRJ48EJVEmSNLF279jBCatWdTLWHdu2dTKOJEmSpPHiJfySJEmSJEmSNIIrUCfI5vXr2bl9eydj3T8729mKHkmSJEmSJGlcOYE6QXZu397ZpOa9Gzd2Mo4kSZIkSZI0zryEX5IkSZIkSZJGcAJVkiRJkiRJkkaYmAnUJKcmuSfJfUnO67seSZIkqStmYUmSpP5MxARqkmXAnwM/DbwKOCvJq/qtSpIkSWqfWViSJKlfk/IQqTcA91XVNwGSfAY4A/hqr1VJkqQl4xuzs52NdcDKlbxmzZrOxtPYMwtLkiT1aFImUF8BPDS0vRX4sZ5qeYbN69ezc/v2Tsa6f3aWE1at6mQsSZL0TLt37Ojs9/Ad27Z1Mo4mxthmYUmSpKUgVdV3DXuV5OeBt1fVLzfbvwS8oarOGTrm/cD7m82jgXs6Ku8Q4LGOxlqq7HE37HP77HH77HE37HP7uuzxD1WVZ2jHmFl4ybPH3bDP7bPH7bPH7bPH3eiqz885B0/KCtStwGFD26uBh4cPqKpLgEu6LAogycaqOrHrcZcSe9wN+9w+e9w+e9wN+9w+e6w5zMJLmD3uhn1unz1unz1unz3uxjj2eSIeIgX8O3BkklcmOQB4F/CFnmuSJEmSumAWliRJ6tFErECtql1JfhP4B2AZcHlVfaXnsiRJkqTWmYUlSZL6NRETqABVdT1wfd91zKPzS6WWIHvcDfvcPnvcPnvcDfvcPnusZzALL2n2uBv2uX32uH32uH32uBtj1+eJeIiUJEmSJEmSJPVhUu6BKkmSJEmSJEmdcwJ1PyVZluTLSa7ru5ZplGRlkrVJvpZkNslJfdc0bZJ8KMlXkmxJcnWSF/Rd0zRIcnmSR5NsGdr34iQ3Jbm3+fqiPmucdCN6/PHm8+LuJJ9LsrLPGifdfD0e+t5vJ6kkh/RR2zQZ1eck5yS5p/mM/sO+6pNGMQe3zyzcPrNwO8zC7TMLt88s3L5JysFOoO6/DwKzfRcxxT4J3FBVxwCvw14vqiSvAD4AnFhVxzJ4MMW7+q1qaswAp87Zdx5wc1UdCdzcbGvfzfC9Pb4JOLaqXgt8HTi/66KmzAzf22OSHAacAjzYdUFTaoY5fU7yFuAM4LVV9WrgEz3UJe2NObh9ZuEWmYVbNYNZuG0zmIXbNoNZuG0zTEgOdgJ1PyRZDbwDuKzvWqZRkoOBNwOfBqiqnVW1vd+qptJy4MAky4EVwMM91zMVqupLwH/N2X0GcGXz+krgzE6LmjLz9biqbqyqXc3memB154VNkRF/jwEuBn4H8Ebqi2BEn38duKiq/qc55tHOC5OehTm4fWbhzpiFW2AWbp9ZuH1m4fZNUg52AnX//AmDfzS7+y5kSh0BbAOuaC4PuyzJQX0XNU2q6tsMzuY8CDwCPFFVN/Zb1VR7aVU9AtB8fUnP9Uy79wJf7LuIaZPkdODbVXVX37VMuaOANyXZkOS2JK/vuyBpDnNw+8zCLTMLd84s3C2zcAvMwp0YyxzsBOo+SnIa8GhV3dF3LVNsOXA88JdV9aPADrzMY1E19x06A3glcChwUJL39FuVtP+SXADsAq7qu5ZpkmQFcAFwYd+1LAHLgRcBa4APA59Nkn5LkgbMwZ0xC7fMLKxpZRZuh1m4M2OZg51A3Xc/Dpye5FvAZ4CTk/xNvyVNna3A1qra0GyvZRAitXjeBtxfVduq6mngWuCNPdc0zf4zycsBmq9jcSnCtElyNnAa8O6q8rKaxfXDDP6TeVfz+281sCnJy3qtajptBa6tgdsZrPLzIQUaF+bgbpiF22cW7pZZuANm4VaZhbsxljnYCdR9VFXnV9XqqjqcwY3Gb6kqz1Yuoqr6D+ChJEc3u94KfLXHkqbRg8CaJCuaMzpvxYcTtOkLwNnN67OBdT3WMpWSnAp8BDi9qv6773qmTVVtrqqXVNXhze+/rcDxzee1FtfngZMBkhwFHAA81mtFUsMc3A2zcCfMwt0yC7fMLNwus3BnxjIHO4GqcXcOcFWSu4HjgN/vuZ6p0qxoWAtsAjYz+Ey4pNeipkSSq4F/A45OsjXJ+4CLgFOS3MvgqY0X9VnjpBvR4z8DXgjclOTOJH/Va5ETbkSPtchG9Ply4IgkWxis8DvbVSTSkmQWbpFZuD1m4faZhdtnFm7fJOXgjEENkiRJkiRJkjSWXIEqSZIkSZIkSSM4gSpJkiRJkiRJIziBKkmSJEmSJEkjOIEqSZIkSZIkSSM4gSpJkiRJkiRJIziBKmlJSvK/Se5MsiXJ3yVZ0eJYv5fkbc3rcxc6VgZuSXJwklVJ/qWp+8yhY9YlOXRo+xNJTl68n0KSJEnTwiwsSQvjBKqkpeqpqjquqo4FdgK/9lze1AS4BX12VtWFVfWPzea5wEID6s8Ad1XVd4CzgCuBk4APNzX9LLCpqh4ees+ngPMWOI4kSZKWBrOwJC2AE6iSBP8M/AhAkt9qzmhvSXJus+/wJLNJ/gLYBByW5Kwkm5vj/qA5blmSmWbf5iQfavbPJPm5JB8ADgVuTXJrkvcluXhPEUl+Jckfz1Pfu4F1zeungQOB7wN2J1nOIIh+fPgNVfUA8INJXrZYTZIkSdJUMgtL0l6kqvquQZI6l+S7VfX9Tei6BrgBuB2YAdYAATYA7wEeB74JvLGq1jeXB60HTmi+dyPwp8BDwEVVdUozxsqq2p5kBriuqtYm+RZwYlU9luQg4G7gmKp6Osm/Ar9aVZvn1PoAcGxVPZnkB4C/BV4KfAR4NfBEVV05z894KXBDVV2zSG2TJEnSFDALS9LCuAJV0lJ1YJI7gY3Ag8CngZ8APldVO6rqu8C1wJua4x+oqvXN69cD/1RV26pqF3AV8GYGwfKIJJ9KcirwnWcroKp2ALcApyU5Bnj+3MDYeHFVPdm854mqekdVnchgBcBpwDVJLk2yNslJQ+97lMFZfkmSJGmYWViSFmB53wVIUk+eqqrjhnckybMcv2P40PkOqKrHk7wOeDvwG8AvAO/dSx2XAb8LfA24YsQxu5I8r6p2z9l/IfAxBveCuoPB2fh1wFua778AeGov40uSJGnpMQtL0gK4AlWS/t+XgDOTrGguKXong3tCzbUB+MkkhyRZxiC03ZbkEOB5zWVCHwWOn+e9TwIv3LNRVRuAw4BfBK4eUdc9wBHDO5IcCRxaVbcxuBH/bqAYBMU9jgK2PPuPLEmSJAFmYUkayRWoktSoqk3NPZpub3ZdVlVfTnL4nOMeSXI+cCuDM/DXV9W65oz7FUNPJj1/nmEuAb6Y5JGq2nN2/LPAcVX1+IjS/h74KeC+oX0fAy5oXl8NfB74IIMz8SR5PoOHAWzc288tSZIkmYUlaTQfIiVJPUtyHXBxVd084vsvB/56zw35n+Of+U7g+Kr66CKVKUmSJC06s7CkSeAl/JLUkyQrk3ydwT2o5g2MMDjLD1ya5OAF/PHLgT/a3xolSZKkNpiFJU0SV6BKkiRJkiRJ0giuQJUkSZIkSZKkEZxAlSRJkiRJkqQRnECVJEmSJEmSpBGcQJUkSZIkSZKkEZxAlSRJkiRJkqQRnECVJEmSJEmSpBH+DzDwfhQu54epAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth Mean =  10.0 , Clustered Sample Mean =  10.49 , Error =  4.9 %\n"
     ]
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "hist_st(sim.flatten(),vmin,vmax,log=False,cumul=False,bins=20,weights=None,xlabel=\"Porosity (%)\",title=\"Porosity Realization\")\n",
    "plt.subplot(122)\n",
    "hist_st(samples_cluster[\"Realization\"],vmin,vmax,log=False,cumul=False,bins=20,weights=None,xlabel=\"Porosity (%)\",title=\"Porosity Samples\")\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()\n",
    "\n",
    "sm_mean = np.average(samples_cluster['Realization'])\n",
    "ex_mean = np.average(sim)\n",
    "print('Truth Mean = ',round(ex_mean,2),', Clustered Sample Mean = ',round(sm_mean,2),', Error = ',round((sm_mean-ex_mean)/ex_mean,3)*100,'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the shift in mean from the truth model to the clustered sample. There is a 4.8% inflation in the clustered sample mean! This will be a good demonstration clustered data set for the value of cell-based declustering. We have created a biased sample set with spatial clustering.  Now we can try some declustering. \n",
    "\n",
    "Let's apply the Python translation of **declus**, the GSLIB cell-based declustering program, to this sample set.  The declus method has the following parameters (df,xcol,ycol,vcol,cmin,cmax,cnum,bmin) where df, xcol, ycol, vcol are the DataFrame with the data and the columns with x, y and feature, cmin and cmax are the minimum and maximum cell sizes, cnum is the number of cell sizes (discretization of this range) and bmin is true for selecting the cell size that minimizes the declustered mean (set to false for the cell that maximizes the declustered mean).  \n",
    "\n",
    "The output from the declus function is a 1D numpy array of weigths with the same size and order as the input DataFrame for the optmum cell size and also the cell sizes and declustered average for each cell size (that's 3 1D ndarrays).  After we calculate the weights numpy array we convert it to a DataFrame and append it (concat) it to our sample DataFrame.  Then we visualize the histogram and location map of the weights.  We will take a wide range of cell sizes from 1m to 2,000m going from much smaller than the minimum data spacing to twice the model extent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 63 data with:\n",
      "   mean of      10.490800374424563 \n",
      "   min and max  6.123025768159604 and 14.16801426820596\n",
      "   standard dev 1.8904816314625261 \n"
     ]
    }
   ],
   "source": [
    "wts,cell_sizes,averages = declus(samples_cluster,'X','Y','Realization',iminmax=1,noff=5,ncell=100,cmin=1,cmax=2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the declustered output.  We should check out the porosity distribution naive and declustered, the distribution and location map of the delucstered weights and the plot of cell size vs. declustered mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DescribeResult(nobs=63, minmax=(0.5025080128205127, 3.360489510489511), mean=0.9999999999999998, variance=0.3499002180795838, skewness=2.0763024101409284, kurtosis=4.037715196963079)\n"
     ]
    },
    {
     "data": {
      "image/png": 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pkNU08tidO3dmRkn+OuXTi1bSqVOnyOPXq9uMpUtL1yj7ZvIqOnfuEXlskbhLqQOZu9Vu27hhwHoHhrv7ve7e0917AkOAz9x9hbvPqFbeBygC3gxPuwf4c7jvlvC9iIiIbOWUgMbBSaecxtszyvl06iJiMWdxfhG3vvk9J555ARkZGZHGbty4Mf2POYa/T/qClcVFlFdW8tGsH5haWc7hRx4ZaWyA8y+/lOfmf8N3Sxbi7szPX87DU0cz+JorI4/drVs30ndpxRszvqK4vIzi8jJen/EVmbu2Zffdd488/oUX3MBttyxjzuyfcXe++XoVjz5Yyrnn/D7y2CJxFyuF4lm12zbC3UcBm9od5AzgpRrKDwV+dPd5VZcFqrp/NAbyajhHREREtjK29pOz7UFOTo5PnDgxoTGXLl3K0088wjcTxtKoSVNOOfsiDjv8iITEdneGv/UWb73wIsVFRfQ75GDOv/hiGjVat2tuFGbPns1TDz/KzO++p2Wb1px3+aX06dMnIbHLysp4cdhzjHz7HQCOOPF4fnvu2ZEn/lUmTZrE8y88yOLFC9h11+6cP+gaOnbsmJDYImY2yd3jMu1yTrd6/uXztes5kJrz7UbrYWYdgBHuvt6uGWZWD8gFdnP3FWvtexr4yt0fDd/vDnxAMH9SCrBfteRUIpKM9lVERLZ+m/ObRAmoiMg2Ku4J6HO1TED3/nYesKxa0ZPu/mT1YzYxAT0NOMvdj1urPIPgCece7r44LHuYoKvu62b2G2Cwux9Wqw+wjQqT8mOBJVXfq5m9AnQJD2kC5IfdlNc+dy6wGqgEKjb175HaVxERqcnm/CbRMiwiIhJOQlTrpYuWxSkRPp2au98eRfD0c3G1snOBqr7+/wGeikP8bc0w4FHguaoCdz+t6rWZ/Q1YtYHzD3b3ZRvYLyIiEncaAyoiIgGv5RYHZtYYOAh4u4bdNY0LzQuPBzgEmBmfmmw7NjS21oKFkH9DzQm9iIhI0ugJqIiIBCIakWFmLwEDgGZmlgsMBdIB3L1q0eCTgA/d/ee1zq0HHA5cvNZlLwIeMrM0oAQYHE3tt1kHAIvdfX2JuQMfmpkD/1i7y7SIiEhUlICKiAhYnU1dUqUG32xwr7ufsbEruPswgi6la5cXATvVUD6aYGkWqdn6ZhOu0t/d88wsGxhpZt+HT1TXYWaDCRP8du3axb+mIiKyQ1ECKiIiwTIsRT8muxYSB+FT4f9jAwm6u+eFfy4xszeBvkCNCWj4dPRJCCYhinuFRURkh6IxoCIiEnCr3SZbm8OA7909t6adZlbfzBpWvQaOAKYmsH4iIrIDUwIqIiIAWKx2myRHOLZ2HNDFzHLN7IJw1zqzCZtZazN7L3zbAhhtZt8AXwLvuvv7iaq3iIjs2NQFV0REgieZtV+GRZJgfWNr3f28GsrygKPD17OBHpFWTkREZD2UgIqISECj+0RERCRiSkBFRCSgJ6AiIiISMSWgIiIS0IRCIiIiEjEloCIiAil1oF7HWp48Ma5VERERke2XElAREYHKUiicnexaiIiIyHZOCaiIiADg6oIrIiIiEVMCKiIiAU1CJCIiIhFTAioiIsEERHoCKiIiIhFTAioiIoFYsisgIiIi2zsloCIiEtATUBEREYmYElAREQmWYWlQ22VYxse1KiIiIrL9UgIqIiIQK4XVc5JdCxEREdnOKQEVERFwNAuuiIiIRC4lGUHN7Gozm2ZmU83sJTPLNLNdzOwLM5tpZq+YWUZ4bJ3w/axwf4dk1HlTFBQUMGbMGKZPn467JzR2LBbj22+/ZezYsRQVFSU0NsDixYsZPXo08+bNS3js8vJyJkyYwIQJEygvL094/Llz5zJ69GiWLFmS8NhFRUWMHTuWb7/9llgssTPIuDvfffcdY8eOpaCgIKGxJSJVM+Fu7iYiIiKyiRL+BNTM2gC/B7q5e7GZvQqcDhwNPODuL5vZE8AFwOPhnyvdfTczOx24Gzgt0fXemBeGDeOdZ5+lV7MslpaUsrJuXe75++M0a9Ys8tjz58/n2sFX0ry4HvWtDneVLuSiG37HcSceH3nsWCzGX4feyVcffkmHtDYsii2jWbcW3PPYfWRmZkYef9LESdx61Z9o761wnPn2E39+6HZ69+kdeeySkhKu+90fyJ++mJapOzG3PI/eR/bjhqFDSEmJ/t7OiLffZtiDD9IraycKystZ4DHueuwx2rVrF3nspUuXcsONF9C8xVKyW8Ajj1Vy3DGX8Nvfnh95bIlOgu+biYiIyA4oWV1w04C6ZlYO1AMWAYcAvw33PwvcSpCAnhC+BngNeNTMzBP9iHEDvvnmG0b9+wX+dvABpKWmAjB54UKGXnctjz0zLNLY7s6Nl13DKXX60a55SwBKK8t4+K7H6dG7Z+TJyFuvv8XCD3/k0tZnYhY8Cfn8+y95+J6HuP6WGyKNXVRUxNArbub8rFNoXKchAKtKC/jT5Tfx2v/epG7dupHGf+juB2gxK5NT2/4fEPy3+M8HH/L2Xm9z0sknRRp73rx5vPTgQ9zd/0Ay09MBmL1sGTdfeSXPvvHGL/8tovLnv1zJoMGr6NW7OQAXDI4x5NrH6N49h7322ivS2BIRN3XBFRERkcglvAuuuy8E7gPmEySeq4BJQL67V4SH5QJtwtdtgAXhuRXh8Tslss4b886rr3JKp11/ST4BerZpQ2FuLitXrow09uzZs6lXkEK7Ri1/KauTmsFBDbrz3lsjIo0N8Obzr3F48wPWSHj2y85h1PufRB57zJgxdLVdfkk+ARrXacTutgtjxoyJPP7nH3zG/tm9fnlvZgxs2Z83n/9P5LH/+/Zwjm3X7pfkE6Bjs2Y0Li1jzpxoJ5JZsWIFxcU/0qt341/K0tJSOOPs+rz77r8jjS0RUxdcERERiVgyuuA2JXiquQuQD/wHOKqGQ6uecNb062adp59mNhgYDCSkC2J1pSXF1Elb96vMTEuLfExiaWkpdSx9nfKM1HRKiosjjR3EL6NOZsYaZSkYXhn9A+rS0lLSY+t+7xmeTllZWeTxPRbD1vrrmZGSTmlR9LGLi4tokbruf/fM1FRKS0sjjV1eXk6dzHX/t6xXN5XSsuj/zklEUutAw11qefLnca2KiIiIbL+SMQnRYcAcd1/q7uXAG8B+QBMzq8om2gJ54etcYGeAcH9jYMXaF3X3J909x91zmjdvHvVnWMPBRx/DB3PWnHwnb9UqiupkEnVdunTpQp7ls6q08Jcyd2dcwXQOPfqISGMDHH7C4YxdOmmNsukrZ9K9b/TdMPfbbz+mxWZRHqv4paw8VsG3FT+w7777Rh5/j5y9+G7lj2uUjVn6NYefEP33fuhRR/Fh7vw1JrtaWVTEvNISunTpEmns7OxsClc3YWHumsnm8Ld+ZsBBJ0YaWyJUWQqr5tZuExEREdlEyRgDOh/oZ2b1gGLgUGAi8AlwCvAycC7wdnj88PD9uHD//7am8Z8AAw4+mM8++IA7x4xnv+xmLCst5dMly7j9sb9HPhYvNTWVIXcP5farbmafzE7Us0wmlswi54QD2HPPPSONDXDWoHO47NNLeC33v3RMactPsWXMa/ATj9/yZOSxs7KyOO/aC3j83qfJydgDgIllU7ng+sE0bdo08vjX3nI9l505mNl5ubRObcasioWUtDWGDDon8th77bUXnQ89hFs/+pgBLVqxuqKcjxcv4oa77458AiQzY8iND/CnGy/gsCNX06IlfPq/GE0aHsCBBx4UaWyJmLrTioiISMQsGbmcmf2ZYCbbCuBr4EKCsZ4vA1lh2VnuXmpmmcDzQC+CJ5+nu/vsDV0/JyfHJ06cGOEnWJe7M2XKFCaMG8dO2c05/IgjqV+/fsLi5+fn88F/P6CwYDUHHHwgnTt3TljsWCzGuHHj+O6bqbTr2IGDDzmYjIyMjZ8YJ3l5eXz0wUgM49AjD6N169YJi11WVsYn//uE+bPn0q1Hd/bdd9+EzIBb5YcffmD0Z5/RsFEjDj/ySJo0aZKw2IWFhYwc+T4rVy6lb98D2GOPPSK/4SJrMrNJ7p4Tj2v16djQv7ijdpdK/+2nG6yHmT0NHAsscffuNewfQHDTsWoA8xvu/pdw31xgNVAJVFSPY2ZXAJcTtCXvuvv1tfoAssmS0b6KiMjWb3N+kyQlAY2aGkgR2RHEPQG9vZYJ6JkbTUAPBAqB5zaQgF7r7sfWsG8ukOPuy9YqPxj4I3BMeLMy290TvxjvDkbtq4iI1GRzfpMkaxkWERHZqkQ3o627jzKzDnG+7KXAXe5eGsZQ8ikiIrINSMYkRCIisrVxgnVAa7PFx75m9o2Z/dfM9lirZh+a2aRwtvMqnYEDzOwLM/vMzPaOV0VEREQkOnoCKiIiAHjtn4A2M7Pq/TKfdPfNmYnsK6C9uxea2dHAW0CncF9/d88zs2xgpJl97+6jCNqvpkA/YG/gVTPruLVNUiciIiJrUgIqIiKQWgdr3KG2Zy/bkrGo7l5Q7fV7ZvZ3M2vm7svcPS8sX2JmbwJ9gVEES3S9ESacX5pZDGgGLK1tPURERCR6SkBFRAQqS/GV8zZ+XATMrCWw2N3dzPoSDA9Zbmb1gRR3Xx2+PgL4S3jaW8AhwKdm1hnIAJbVcHkRERHZiigBFRGRYKRlRJMQmdlLwACCrrq5wFAgHcDdnyBY4/lSM6sgWB/69DAZbQG8GS7vkwb8293fDy/7NPC0mU0FyoBz1f1WRERk66cEVEREAIgqfXP3Mzay/1Hg0RrKZwM91nNOGXBWXCq4jappfVUzuxW4iF+7It/k7u/VcO5A4CEgFXjK3e9KSKVFRGSHp1lwRUQk4LXcJFmGAQNrKH/A3XuGW03JZyrwGHAU0A04w8y6RVpTERGRkBJQEREBarkES/yWYZHNFM4GvKIWp/YFZrn77PBJ8svACXGtnIiIyHooARUREfBgGZbabLLVudzMvjWzp82saQ372wALqr3PDctEREQipzGgIiICaRlYkw7JroVsuceB2wg6R98G/A04f61jarprsN7O1GY2GBgM0K5du/jUUkREdlhKQEVEBCrKkrYMi8SPuy+uem1m/wRG1HBYLrBztfdtgbwNXPNJ4EmAnJwcjfoVEZEtoi64IiICqAvu9sDMWlV7exIwtYbDJgCdzGwXM8sATgeGJ6J+IiIiegIqIiIBTSi0TVnP+qoDzKwnQZfaucDF4bGtCZZbOdrdK8zscuADgmVYnnb3aUn4CCIisgNSAioiIuAWbLLNWM/6qv9az7F5wNHV3r8HrLNEi4iISNSUgIqISLCkp0b3iYiISMSUgIqISEBdcEVERCRiSkBFRARLy8Cy2ie7GiIiIrKdUwIqIiJ4RRmx5fOTXQ0RERHZzikBFRGRcBCouuCKiIhItJSAiogIAK4xoCIiIhIxJaBxUlZWxnvvjOCLT0eRld2ck888g44dOyYs/nfffcfrL75OwcpVHHLsoRxx5BGkpqYmJHZBQQGvv/I6UyZMZZcuHTjt7NPIzs5OSGx3Z/z48bzz8ggAjj/jOPbZZx/MEvNDesmSJbzy/CvMmTGXvfbZk5N/czINGzZMSGxJnsmTJzPi3RcpKythwEEnMmDAwaSkpCS7WltIy7CIiIhI9Lb1X0xbhbKyMi47dxDfDfsPR5Rm0v77PG4aNJhRn36akPhvvfYmN589hMaj0un6fXtG3PoGV198FbFYLPLYK1as4Ozjz2Ha4zPZdWo3Fr+4kvOOO5+ZM2dGHhvgb3c+wEOX/p2ssa3JGtuaBy9+lAfueiAhsX/44QcGHTeI5S8tp+v0rkx/fAZnn3AOK1asSEh8SY4XX/wXTz51AQMO/4LjT57KZ6OHcNtt1+HbwRom7larTURERGRTKQGNg/feGUHHwhind+tD+6bNyGm7C0N6DeCRO+6KPAksLS3lqfv+yaCWZ9ClaSfaNmjN8S0HUvztasaOHRtpbICnHn2KvQr60K9pf1rUbUnPpn0YmHI89w69L/LYubm5fP7aaE5qchrtGnSgXYMOnNT0dD599XMWLlwYefx7h97LsWnH0SurNy3qtmC/rH3pWdCTf/29xnXgZTuwatUqRrz3D/56X2t69mpC190bct2QluSv/pxp06Ylu3pbxglgVE3MAAAgAElEQVSWYanNJiIiIrKJlIDGwRefjqJfqzWXL2hYJ5MsS2fx4sWRxp4xYwZtUlqRkZq+Rvkedbow/tNxkcYGGPfpeLo37rFGWet6bVgwMzfy2JMmTaJjRec1utuaGR0rOjNp0qTI4+fOWkjr+q3XKNuzyZ6M+yT6712SY8qUKfTtl0pq6ppJ14BDUpgwYXSSahU/7rXbRERERDaVxoDGQVZ2c5Z9n0eHps1+KXN3VpYWRz4eMCsri1WVBeuUryxfRYeWXSKNDZDVLIv8vJXsVOfXz15WWUZanejHn2ZlZVGYvu5nL0xbzU477RR5/NSMFMpj5aSn/Jr855flk9UqK/LYkhxZWVks/mnd8p8WQctmLRJfoXhKy8CatUt2LURERGQ7pyegcXDymWfw+oLvWV1aAgTJ50dzvqdLTi8aNGgQaey2bdvSeLemTF0x/Zey/NJVTPJvOf6k4yONDXDOZWfz0er/Uh4rByDmMT5ZNZITzzox8tj77rsvSxrlkVf069PWvKJcljbOo1+/fpHHP/GsE/loxUfEPOhmXR4r54OCDzjnsnMijy3Jsfvuu7N4UTO+/mrVL2W5C4r5+MMUDj/8yCTWbMt5RRmxpQtqtYmIiIhsKj0BjYOOHTty+W23cPvtfyXL0skvLabL3r256bY/JyT+PX+/lz9dczOfT/mSuil1KG9YyW1P3JGQp4AHDTiIn677iWcf+SeNvSn5vpIjTz+cQYPPizx2Wloajzz3MDddcTM/LywGnAZt6/PoI48kZAbg8y8+n9WrVvPP15+kSUpT8i2fc687hwMPOjDy2JIcZsY9dz/NX267imH/nEVGHaO4KIvb/vIM9erVS3b1toxrPKeIiIhEz7aHmRvXlpOT4xMnTkx43FgsxuLFi2nYsGHkTz5rUlBQQHFxMdnZ2QlbhqRKeXk5S5cuJSsri8zMzITGBli+fDlAQpLutZWUlLBixQqaN29Oenr6xk+Q7cLKlSspLy9P2JJDNTGzSe6eE49r9WqT5Z9edlitzm1y83/iVg/ZuiWrfRURka3b5vwm0RPQOEpJSaFVq1ZJi9+oUSMaNWqUlNjp6em0bt164wdGJBmJZ5XMzMykfnZJjqZNmya7ChHQE1ARERGJlhJQEREBwNUFV0RERCKmSYhERCTgVrttI8zsaTNbYmZT17P/BDP71swmm9lEM9s/LG9vZpPC8mlmdkm1c943s2/C8ifMLPqB3yIiIrLF9ARUREQgLYOUZm2juvow4FHgufXs/xgY7u5uZnsBrwJdgUXAfu5eamYNgKlmNtzd84DfuHuBBQPeXwNOBV6O6gOIiIhIfCgBFRERKC+jcmnuxo+rBXcfZWYdNrC/sNrb+oCH5WXVyutQrdeOu1ctApwGZFSdIyIiIls3dcEVEREcw712WzyY2Ulm9j3wLnB+tfKdzexbYAFwd/j0s2rfB8ASYDXBU1ARERHZyikBFRGRQO3HgDYLx25WbYM3O7T7m+7eFTgRuK1a+QJ33wvYDTjXzFpU23ck0Irg6eghW/jpRUREJAHUBVdERIAtmgV3WbzWAQ276+5qZs3cfVm18jwzmwYcQLWnne5eYmbDgROAkfGog4iIiERHT0BFRAScpHXBNbPdwsmEMLPeBGM6l5tZWzOrG5Y3BfoDM8ysgZm1CsvTgKOB77e4IiIiIhI5PQEVEZFAnMZzrs3MXgIGEHTVzQWGAukA7v4EcDJwjpmVA8XAaeGMuLsDfzMzBwy4z92nhN1wh5tZHSAV+B/wRCSVFxERkbhSAioiIlh6BqnZ0SzD4u5nbGT/3cDdNZSPBPaqoXwxsHfcKigiIiIJowRURETw8jIqFkezDIuIJN6U8eMpy8/f4utkNGnCnv36xaFGIiIBJaAiIgKAx5JdAxGJl7L8fPo0b77F15m0dGkcaiMi8isloCIiUn1JFREREZHIKAEVEREc4jKjrSSOmT0NHAsscffuYdm9wHFAGfAjMMjd1+mHaWZzgdVAJVARr2V0RERENkbLsIiICJC8ZVik1oYBA9cqGwl0d/e9gB+AIRs4/2B376nkU0REEkkJqIiIBKq64W7uJknh7qOAFWuVfejuFeHb8UA0UxuLiIjUkhJQEREBB49ZrTbZap0P/Hc9+xz40MwmmdngBNZJRER2cBoDKiIiWHoGaS3aJLsaEidm9kegAnhxPYf0d/c8M8sGRprZ9+ET1ZquNRgYDNCuXbtI6isiIjsOJaAiIoKXl1PxU16yqyFxYGbnEkxOdKi7e03HuHte+OcSM3sT6AvUmIC6+5PAkwA5OTk1Xk9ERGRTqQuuiIgA4F67TbYeZjYQuAE43t2L1nNMfTNrWPUaOAKYmrhaiojIjkwJaJyt52ZzwmInO34yYyc7fjJjJzN+Mul7jy/NgrttMbOXgHFAFzPLNbMLgEeBhgTdaieb2RPhsa3N7L3w1BbAaDP7BvgSeNfd30/CRxARkR1QUrrgmlkT4CmgO8FECOcDM4BXgA7AXOA37r7SzAx4CDgaKALOc/evklDtDZo+fTr33vJXli1YjKcaR596HIOvuITU1NTIY5eVlfHgXQ/y8TufYJVGh27tGXLHjbRv3z7y2ACjPh3FQ3c8xuqlP5NeP5VBvz+HU047OSGxV65cyZ1/upuvx3wDQO/9ezLkL9fTtGnTyGO7O6+9/DrPPvY85T9X0rB5fa784+UccNABkccGmDdvHvfccju5M+fgBgcdfThXXHc1GRkZCYmfTF9NmsSjd91FwdKlkJbO8WeczlmDBpGSEv09taKiIh669w6+GvcJhtOh855cc9NfaNmyZeSxo6R1QLc97n5GDcX/Ws+xeQTtKO4+G+gRYdVERETWK1ljQB8C3nf3U8wsA6gH3AR87O53mdmNwI0E3YiOAjqF2z7A4+GfW42ffvqJG86/mnOaH0Grjs2piFXw7utjeKDwb1x78/WRx7/5mj9RMTqFQU0uI8VSyP1hPpedcTkvf/BvGjZsGGnsyZMnc9cVD3Jknd/SILMhpWUlvPTn10lJSeH/Tj0p0tixWIxLzvodHeb25vQGVwIw839TuWT273jpnRciT0be+M+bvHrncE5sMIg69TNZvaqAv17xNxo934gePaL9bVdQUMBV517MWc33YdfuOcQ8xoeffc3QpTfx1wfvizR2ss2ePZt7rr2W63rvTcvuPSitqOCZt4YzrLyC8y+5OPL4Q665lIOzF/OHS7uQkmJMmrWYay45m2GvvrttJ/+OllQRERGRyCW8C66ZNQIOJLxL6+5l7p4PnAA8Gx72LHBi+PoE4DkPjAeamFmrBFd7g159/mUOrteDVg2aA5CWksZxbQ9g1LsfU1xcHGnsJUuW8MO4WfRruj8pFvznbFu/HZ2L9mDE2yMijQ3wj/v/xf6px9IgLUh066Rmckj9k3j2sRcijz1x4kRSFzSkc8M9MTPMjM4N9yR1QX2++ir6h+TPPfYCRzQ4iTqpmQA0TG/EgPRjefL+Gh9AxNW7w9+hX5327Nq0NQAplsLADn2YO2k6S5YsiTx+Mr30zDOcuVtnWjZqBECdtDQu6NWbd199lVgsFmns2bNnk7JqDsfu3ZqUlCBZ67PbTvTfuZJPPvlfpLETQuuAioiISMSS8QS0I7AUeMbMegCTgCuBFu6+CMDdF4VTwwO0ARZUOz83LFtU/aLJnCZ+wY/z2Lv+mmt9p1gKO6U3ZuXKldStWzey2D/99BM7WfN1ypunZjP/xwU1nBFfC+ctpHedo9coy0ytR8nqsshj5+Xl0bikWfD8vJrGpdnk5UU/m2dJYRmZa/23bZHZmjHz31vPGfGz4Me5tK2XtU55qzpNWLx4MdnZ2TWctX1YOHcuJ7TeeY2y9NRUGqSmUFxcTP369aOLvXAhuzVf9ynnbtkZLJw/N7K4iWDpGaS1bJ3saoiIiMh2LhmTEKUBvYHH3b0X8DNBd9v1qen2+jozf7j7k+6e4+45zZuvm5BFqUe/XkxfNXeNsrLKcpbHVkeeCHTs2JFFnkvM13zyM7dyNj33iX6IT4+992RO4Yw1yvLLlrNT6+jHYO6xxx4sqjtnnfJFdefQrVu3yOPv1KoJ+WUr1ij7cfUM9srZM/LYPfr24buCNZPsmMeYU7yUXXbZJfL4ydQ9J4ev8nLXKFtdUkJpWhr16tVbz1nx0aVLFybNK15n8qEv5pbQvUfvSGNHzcvLKV+0qFabiIiIyKZKRgKaC+S6+xfh+9cIEtLFVV1rwz+XVDu++uOOtsBWtVjdSaeezLS6uYxZNJmSilIWFi7hqblvM+j3F5KWFu1D5gYNGnDCecfx1spXWV6yjJKKYsav+Jyf2+dzyKGHRBob4OKrLmJSvY+YtXoaFbFyFhTNZmTly/xh6JWRx+7UqRO77d+WUQUjKKxYTWF5AZ8VjKDT/juz2267RR7/mqFX8V7py8z/eQ7lsTJmFEzly8yPueSqwZHHPuSwQ1nQuJQP5n5FUXkJiwtX8o/pH3LMmf9HgwYNIo+fTKeffTYjlizms9mzKCkv58dlS/nrF2O56JprCOYsi052djbd9xvI3W//yE8ri1j1cxnD/jePpant2HvvvSONnQiaBVdERESiZslYRsDMPgcudPcZZnYrUNVnbnm1SYiy3P16MzsGuJxg9r59gIfdve+Grp+Tk+MTJ06M8BOsKz8/n+eeGsa4T8bQLLsZvx18Dvvuu29CYrs7H3/0MS8/9QqFBYUcetwhnHnemZE/DaqycOFC/vXYM3w7cSrtd23HRVeeT9euXRMSu7KyktdffYO3XwrGu5545nH83yknJWT2YQhmP/7XI88wb9Z89szpzoWXn0/r1onpxlhUVMSLw55n1Psf0bBxI04+9wwOPeywhMROtmXLlvHcU08xefwXtGjTmjMvuoiePXsmJLa78967I3jvjRcpKy1hwJEncurpv03KBERmNsndc+Jxrb2ys/2dk0+p1bkdnng8bvWQrVsy2lepnUnvv0+fOPQIm7R0KX0GDoxDjURke7Y5v0mSlYD2JFiGJQOYDQwieBr7KtAOmA+c6u4rwmVYHgUGEizDMsjdN9j6qYEUkR1BXBPQ5tk+/ORTa3XuLv/4uxLQHYTa122HElARSaTN+U2SlGVY3H0yUFMFD63hWAd+F3mlRER2dDF1pxUREZFoJWMMqIiIbGUcjQFNJjPrnuw6iIiIJEJSnoCKiMjWxdLTSW+1VS2xvKN5wswygGHAv8P1sUVERLY7SkBFRAQvr6As76dkV2OH5e77m1kn4Hxgopl9CTzj7iOTXDUREZG4UgIqIiLgqDttkrn7TDO7GZgIPAz0Cifiu8nd30hu7UREROJDY0BFRASIbgyomT1tZkvMbOp69nc1s3FmVmpm127KuWZ2qplNM7OYmW3zM/Ca2V5m9gAwHTgEOM7ddw9fP5DUyomIiMSRElAREQm41W7buGEES2mtzwrg98B9m3HuVOD/gFGbUoHaMLNWZnaKmd0fVYxqHgW+Anq4++/c/SsAd88Dbk5AfBERkYRQF1wREQHAYxFd132UmXXYwP4lwBIzO2ZTz3X36QBBD9UtZ2YpwJ5Af2Df8M90YAwwNi5BNuxooNjdK6vVJ9Pdi9z9+QTEFxERSQgloCIigmsM6ApgOTAi3Ia4e24C438EHAYUhu/rAR8C+yWwDiIiIpFTAioiIsAWrenZzMwmVnv/pLs/GYdKJdJD/PrkszHQ2MzGAtPc3RMQP9Pdq5JP3L3QzOolIK6IiEhCKQEVEREsI42MNi1re/oyd9+mJwJy96EA4ayz3QmS0WuB7ma2zN03NIY1Hn42s95VYz/NrA9QHHFMkW3KlPHjKcvf8iVyM5o0Yc9+/eJQIxGpDSWgIiKCl1VQmrs42dXYGjQB2gJtgJZAKTA/AXGvAv5jZnnh+1bAaQmIK7LNKMvPp0/z5lt8nUlLl8ahNiJSW0pARUQEiG4MqJm9BAwg6KqbCwwlmOAHd3/CzFoSrH3ZCIiZ2VVAN3cvqOlcd/+XmZ0EPAI0B941s8nufuQW1PEZgkmI8oFxBJMP3e/uq2p7zc3h7hPMrCvQBTDge3cvT0RsERGRRFICKiIigYhGOrr7GRvZ/xPBU8dNPtfd3wTe3PLa/WIfgqef84BVQAGJ7wK7N9CBoG3uZWa4+3MJroOIiEikNpqAmlkmcCxwANCaoEGeCrzr7tOirZ6IiCTEDj4Lrrt3M7Msfp2I6E5gLzP7Hhjr7tdGGd/Mngd2BSYDlVXVApSAiojIdmWDCaiZ3QocB3wKfAEsATKBzsBdYXL6B3f/NtpqiohIlJwdOwEN9QEOJvg67gA+AXoRJKRRyyHodpyIGXdFRESSZmNPQCe4+63r2Xe/mWUD7eJbJRERSQaPJbsGyWNmVwC/BZ4GhhCMR+3h7vcBExJQhakEkx4tSkAsERGRpNlgAuru725k/xKCp6IiIrINS0lPp07bWi/Dsj24CNjX3X82s0vd/WIz+xK4L0HxmwHfhTFLqwrd/fgExRcREUmITZqEyMxygD8C7cNzDHB33yvCuomISILEyiso2cGXYXH3n8OXFq4HmpHA8LcmMJaIiEjSbOosuC8C1wFTgB24k5aIyPZrBx8DutrMWrt7HlAfGE58Z9ndIHf/zMzaA53c/SMzqwekJiq+iIhIomxqArrU3YdHWhMREUmeHXwWXOAsoCx8/VdgpruPTlRwM7sIGAxkEcyG2wZ4Ajg0UXUQERFJhE1NQIea2VPAx6w5NuWNSGq1jVqyZAlff/01TZs2JScnh5SUlITFrqio4Msvv6SwsJC9996bpk2bJiw2wJw5c5gxYwY777wz3bp1I+i9lhhFRUV88cUXAPTr14+6desmLLa7M23aNHJzc+natSsdOnRIWGyAlStXMmHCBBo0aEDfvn1JS0vc0r6xWIyJEyeSn59Pz549yc7OTlhsgLy8PKZMmUJ2djY9e/ZM6N+5srIyvvjiC8rKyujbty8NGzZMWOyo7Oiz4Lr7nGqvn0lCFX4H9CWYcR53nxlO9LdBZvY0wVJpS9y9e1iWBbxCsKboXOA37r6yhnPPBW4O397u7s9u+ccQERHZsE39tToI6Aqk82sXXAeUgIYef/AxPnrpPbqmd6CAQu6tv5qHnnmU1q1bRx575syZXH3+tbQobEudWF3uT3mY8645i9PPPj3y2JWVlQy56o98P2ou2WUdWJWxmPqdUnjs2YepX79+5PE/HzWav1zzV1oXdwGcOzLv5dYHb2L/A/aPPHZhYSG/O+/3lMyK0bSsBT/VGUaXA3bhzgduJzU1+p5zLz3/Ei889CK7xTpRklLCXfXu4v6n72e33XaLPHZeXh5XDrqc7MLGNKY+j1c8zBG/PZpLr7ws8tjuzr233cWk90fTLXNnlsUKWNGogkeGPUFWVlbk8b/55htuv/Zq+uzUkDopxj/uXMmgq6/lqGOPizx21HbkBNTMCgjnN6he7O4NzWy0u0f9j0qpu5dV3Ugxs7S16rI+w4BHWXO90BuBj939LjO7MXx/Q/WTwiR1KMHyLw5MMrPhNSWqIiIi8bSpCWgPd98z0ppswyZMmMC4lz7h9+1PJ8WCp56zVy3g5quG8PSr0d5Qdneuv2QIR5T9H80aBTfL+8UO4oX7nqZv/7507Ngx0viv/PtVFn9czLENzw9WiAWmTZ/A3+54gFvuvHnDJ2+hwsJC/nzVXzk25ULqNgiS3eKKA7n1yjt5+/P/RJ4A33fb/TSb0ZE9G+4N9YKyTz8ewasv/4czzow2+f/xxx95+f5XOG+nC0hLCf43XlK8hBsuuYHXRr4W+dPAP/7+Ro7y/dil1c4AHOr9efqF1+i7/z706dMn0tgfjfyIuR9+wx86nfzL5/x22Y/cduNQHnjykUhjV1RUcPt11/CX/j1o3jD4+3VKeTnX3X8vffruk/CnwPFlsAMnoO7eaAP7or+jBZ+Z2U1AXTM7HLgMeGdjJ7n7KDPrsFbxCcCA8PWzBGt537DWMUcCI919BYCZjQQGAi/VrvoiIiKbZlP7iI43s26R1mQbNuKVtzm4Se9fkk+Ajo135ufcfFasWBFp7JkzZ5KZX59mmb/+8E1LSaOH9+XdN9+LNDbAmy8Mp3e9AWuU7V6/D59/OC7y2GPGjKFdSTfqpv6aaNZNq8/OxbszZsyY6ON/PJ49GqyZbPWtfzBvvRD9cOl333iXPqk5vySfANl1s6lbUJ9Zs2ZFGnv58uUULVzNLo12/qUsxVI4qPHeDH/5rUhjA4x4+U2ObNlnjSR7r2a7MmfKD5SVlW3gzC339ddf071JvV+ST4C66ekc1b4FH33wQaSxo5aSnkZm2xa12rYHFjjFzB40swfC14nMyG8ElhJM9ncx8B6/do/dXC3cfRFA+GdNd0baAAuqvc8Ny0RERCK1qU9A9wfONbM5BGNAtQxLNRUVFWskn1VSSKGysjLS2LFYjJQa7iOYpVBZEW1sgJg7KWtN1GgY+Kb0HNsylZWVEFv396El4HsHMA8/azUplkIsFv1E0ZWVlaTYut18UxMQv7Kycp3PHcROTcjfucrKSlJr+v/NLPLPHovFaowdfO/Rf/YoxcoqKJ6/4y3rbGafEIyhvBdoC7we7joPOITgSWTk3D0G/DPcEqGm5LrGf7jNbDDBBEm0a9cuyjqJiMgOYFOfgA4EOgFHAMcRNNbb/oCnODnqlGMZnT8Zr5Z05RUuIa15Js2bN480dufOnSmot4JVZb8O24l5jClMYOAJR0YaG+CYU45k8s9rThT5Q+G37H1gtN0wAfbff38W1P2OssqSX8rKKktYkDmd/v37Rx6/zwG9+KFwyhplXxWO5tjTjo489sATBvJ1xSRi/mvClV+6kvy6+XTq1CnS2NnZ2aQ2r0Ne4a9rRro7Ywq+4uhTj400NsDAU47jf4u/XqNs5ooFtOrUjszMzEhj9+rVi8nLC1hV/OvfufLKSj6Yv5hDDj8i0tiJ4G612rZxTcL1Pw8ETnD3Z8PtWOCARFXCzOaY2ey1t1pebrGZtQqv2wqo6c5CLrBztfdtgbyaLubuT7p7jrvnRN2miYjI9m+DT0DNrIG7F7r7vI0dE/+qbTv69+/P6KNG8fh/X2OPtF1Yxc/MTMvjgWeiHY8GkJKSwh2P3cb1F97ILiu7klFZl1np0zjhwqPp2rVr5PHPOu9Mvhx9NSMnv0TzkvasylxMWft8/jn08chjN2rUiGtu+x1/++OjdFgdPIyfW/dbrr3zCho1Wu9wrri5fui1XDzjMhbmzaZJaQt+qjOHrD4NOOOs6Cd/2n333Tni/MN59pmn6VrZjeLUYmal/8A9T92dkNmXb3vwDq45/0p2LWhNIxrwXeVseh/Tl3333Tfy2EcfczTjPvmcx74czh4ZbVkaW83sjJU8ctcTkcfOyMjg2tvu5IY/3siAVllkpqbw8cJlnHTBxQmZcCxq20EyWRvp4YQ8PxB0VV0MYGYtCGaQTZScaq8zgVMJlmSpjeHAucBd4Z9v13DMB8CdZlY1ZfoRwJBaxhMREdlk5hvoKmlmHwOTCRqvSeFdYsysI3Aw8Bvgn+7+WgLquslycnJ84sSJCY87e/ZsJk6YwE7NmnHAAQeQkZGRsNhFRUV8+umn/Fz4M/v13482bRI3lMfdmTJlCt999x3t2rWjX79+CV2CZsWKFXz22SgABgw4KKFL0MRiMcaPH8/8+fPp1q0be+65Z0KXA1m4cCFjx4ylQcMGHHTQQdSrVy9hscvKyhg1ahQrli9n77592WWXXRIWG2DGjBlM/vprWrZqRf/+/RO6BM3q1av57LPPKC0p4YADD0za5ENmNsndczZ+5MZ1a9L6/9m78/ioqvOP458nCSQEAmHfERTQquBCRKhLcUeqoP25gPu+W621rWi1aqvivtQFqWjdF9QqVRR3xQ0IKIKiCIiA7GuAEEIyz++Pe0OHECAkuTNZvm9e88rMuctz7p1hZp45557jTx14QYW23e/Nm6qsHolmZqcDNxAkoH0IBuyB4DPuC3f/bZKqRnlG3zWz5wkGHGpBkDz/DXgNeAnoBMwFTnT3FWaWA1zk7ueF254DXBvu6pbyTD+TrM9X2XGT3n6bXlXQYj1p6VJ69e9fBTWqvNp4TCK1xY58J9lmAhrubABwKnAA0BQoAn4A3gRGuvuiylW36ukDUkTqgqpMQH/VpJ0/deCFFdq295gba2wCChDOt9kDaFJ6WaLmuzazfeMephC0iF7s7nslIn556fO15qiNyVptPCaR2mJHvpNst8nA3ccQjMYnIiK1Vq24nrNC3H0J8H6Sq3F33P0igu6/JyWnKiIiItFJXJ81ERGp1upqAgoQDviztZFhzd0j7WPu7odEuX8REZHqQgmoiIiQUj+NjE7JuZa1mojvNpQOHE8wKNEDiQhuZldta7m735OIeoiIiERte6PgjgEucfc5iamOiIgkQ6ywiPU/L012NZLG3VeUKno4vJ7lxgRVIQfYj2AEWwimOvsEmJeg+CIiIgmxvRbQfwPvmNmTwB3uvjH6KomISMI5eKxOd8GNn7w4FehFYnsJtQD2dfc1YX1uBEaVjFgrIiJSW2zzw9XdXzKzNwmGqM81s6eBWNxydQkSEakFnLp9DShwZ9z9kkGATkxg/E5AYdzjQqBzAuOLiIgkRHl+3d0IrCO4JiaLuARURERqi7o7Ci6Aux9auszMdkpgFZ4GJpjZfwh+DzgeeCqB8UVERBJie9eA9gfuIbgmZV93z09IrUREJOHqcgJqZt0IrrvMiiu+yMyGAx+5+8dRxnf3W8zsLeCgsOhsd/8qypgiIiLJkLKd5dcBJ7r7NUo+RURqN3er0K08zKy/mf1gZjPN7Joylncysw/N7Csz+8bMBoTl9czsSTObambTzWxo3DZXmNk0M/vWzK6s5OG/DDQG1sTdioC1bN41NkqZQJ673w/MN0RMJI0AACAASURBVLNIp34RERFJhu1dA3rQtpaLiEjtkFI/jQadW0SybzNLBR4CjgDmAxPNbLS7fxe32l+Bl9z9ETPbHRhDcA3kiUC6u/cws0zgOzN7HmgEnA/0JkgQ3zazN939xwpWs7j0iLdmdpq7313B/e0QM/sbwUi4uwJPAPWAZ4ADEhFfREQkUTQPqIiIENtQRP5Py6PafW9gprvPBjCzF4BBQHwC6gQtkABNgAVx5Q3NLA1oQJBs5hFMWfJlSe8cM/uY4LrJOypYx8vLWRaV44F9gMkA7r7AzLK2vYlI9GZNn17pfdTPzqZHnz5VUBsRqQ2UgIqISNSj4LZn8/ks5wP7l1rnRoJpvy4HGgKHh+UvEySrCwm6qP7B3VeY2TTgFjNrDqwHBgC5lajjVWa20N1nm9nDwIHAzZXY344qdHc3Mwcws4YJjC2yVbF16+jVsmWl9jFpad2dY1hEtrS9a0BFRKROqNj1n2HS2sLMcuNuF2yx8y15qcdDgH+7eweCZPJpM0shaD0tBtoBXYA/mtnO7j4duB14F3gbmEJwzWZFdQuTz/2ArsBRwN8qsb8d9ZKZPQpkm9n5wHvAvxIYX0REJCHUAioiIkClWkCXuXvONpbPBzrGPe7A/7rYljgX6B/Uw78wswygBXAK8La7bwSWmNlnBNdKznb3kcBIADO7NYxTWccQXIu60Mwqk9DuEHe/y8yOIOhevCtwg7u/m6j4IiIiiaIEVEREAPDSbZJVZyLQLRzV9RdgMEFiGW8ucBjwbzP7FZABLA3LDzWzZwi64PYB7gMws1buvsTMOgG/A/pWoo7vmdkkoCWwt5k1BlZXYn/lFg7SNNbdDydo0RUREam1lICKiAh4dAmouxeZ2WXAWCAVeNzdvzWzm4Fcdx8N/BH4l5n9IagNZ4XXRD5EMCrsNIKuvE+4+zfhrl8JrwHdCFzq7isrUcerzKwnMC9uP/0qur8djF1sZvlm1sTdE5L0ioiIJIsSUBERISU9jcyKTsPy2fZXcfcxBFOrxJfdEHf/O8qYcsTd1xJMxVLWPqt0qrC4xDYZCoCpZvYusK6k0N1/n7wqiYiIVD0loCIiQvGGItZGNw2LbN+b4U1ERKRWUwIqIiJApNOwyFaYWSd3n+vuTya7LiIiIomgaVhERASgMtOwSMW9VnLHzF5JZkVEREQSQS2gIiJCyTygknDxJ33npNVCREQkQZKWgIbDzucCv7j7MeHw/C8AzYDJwOnuXmhm6cBTQC9gOXCyu89JUrW3Kj8/n5dfGMUX739K89YtGHLuaeyxxx4Jiz9x4kRefHwUeavyOHzQYRz3u0HUr18/IbGXL1/OM48/x5SJ09i5+06cccFpdOrUKSGx3Z133nmX1597A4DjTj2WI444HLPEfJGeO3cuT414hp9mzGGv3j057ZxTaNasWUJiFxYW8vqrr/P+6PfJys7i5HNOJidnW1MxVq1p06bx/MhnWbF0Ob8+7ED+7+QTyMzMTEjsvLw8XnruBSZ/OoHWHdpyyrln0K1bt4TEBhg3bhxvjnmawsIC+v3meH7724GkpqYmLH4U3NUFN0l8K/dFRERqpWR2wb0CmB73+HbgXnfvBqwkmJSc8O9Kd+8K3BuuV62sX7+e8wafzawncum/dh92npbF387+C2Pfejsh8Z/993Pceu5dtJq4C3v9dCDv3/o5F595KcXFxZHHXrJkCacfew6zn8hjj5mHkfefDM477lK+/fbbyGMD3HTtPxj5h1G0n5JD+yk5PHblS/z9ulsSEnvatGmcP+gS8l9PZ6+fDuPnf6/i9GPPZsmSJZHHLi4u5uIzLuHj28ex3/w+dPy6M8POu53nnnou8tgAb735Fn87eyhdpjbl8FW9+OGxSZw/+BwKCgoij52Xl8f5J53BipenMmhjDzpPM4ae8Xs+/7QcQ7FWgUdH3Mfrb/yBk0+byQWXLuT7mbcx9NpL8Agn0UwUj1mFblIpe5lZnpmtAXqG9/PMbI2Z5SW7ciIiIlUtKQmomXUAfgs8Fj424FDg5XCVJ4HjwvuDwseEyw+zRDVvldN/XxtNl5XNObxdH5plNKFb0524oOPxPHzbA8RisUhj5+fn8/SDz/G77NPp0LATTdOb85umR1L8XSqffPxJpLEBHr3vX+y+6iD2yNqPrHrZdG20J/1iJ3LHDfdEHnvOnDnkjpnKoY1OoEV6G1qkt+HQRicw/o0p/Pzzz5HHv+P6ezgi5SS6Z+1J43rZ9Gi8H3uuPoARDzwWeeyPP/qY1B/TOKzlETTLaE7HRp04ucUpPPPgs+Tn50cau7i4mIeH/ZPz2p/ILtk70Swjm8Pa/pqOy1vwxug3Io0N8OIzz9OrqBOHdNiXZg0as3uLLlzUeQD3/f3OyJPAFStWMG7cs9xwc1u6dW9Eh44NuOTyNqSkfc3XX38daexE0DWgiefuqe7e2N2z3D0tvF/yuHGy6yciIlLVktUCeh/wZ6AkO2sOrHL3ovDxfKB9eL89MA+CycyB1eH61cbET8bTo3HXzcoapGXQ1BuxaNGiSGPPnDmTNt6eVNu8+98uKbsxYVxupLEBJnw2ia6N9tysrEV6Gxb+tDjy2FOmTKFtfrfNutuaGe02dGfKlCmRx188dwkt0ltvVrZr1p5M/GxS5LEnjJtI17TNu5ympaTRlnbMmjUr0tiLFi2imTcmIy19s/I9s7ox4aMvIo0NkDvuS/Zpsfn/tybpjbB1RZEn39999x29eqeRkrJ50tX3AOOrr76MNHbUUtLTaLRz8wrdRERERMor4deAmtkxwBJ3n2Rm/UqKy1jVy7Esfr8XABcACbv+sESbjm1Z8sMK2jf6XzLi7qzcuIYmTZpEGrtFixasZuUW5Stiy9in066RxgZo3bYVK79fSvO4RKywuIB6mdG/tFq2bMm6jC2PfV39FbRq1Sry+GkZqRQWbaB+6v8SsRUbltK6S/Sx23Zqw/SiGVuUr/KVtGzZMtLY2dnZrC7Kw903S/6XFCyjTcd2kcYGaN2uDYtmrCA7I2tTWXEsRn6skIyMjEhjt2zZkl/mb1k+fz7s1K79lgtqkNiGItbMWpHsaoiIiEgtl4wW0AOAgWY2h2DQoUMJWkSzzawka+kALAjvzwc6AoTLmwBbfEty9xHunuPuOVF/AS/tpNMH8/66SawsWA1AzGO8v2g8+x7Sm4YNG0Yau127drTv2ZpvVudu6n64rGAJ36d/zcDjj400NsB5V5zFpxv/S2FxcO1fcayIcflvMOS8EyOP3adPH/JbLWFu/sxNZXPzfyS/9TJ69+4defzB557Ix+veoCgWNNxvKC7g48I3OPeKsyKPPfD4gXyT9jVL1y8Fgh88Jq+cRPue7WjTpk2ksRs2bMje/XL4cPEXxDzoxLCyYDUfr5/ESaefHGlsgFPOP5PXl45nTWHQ2hnzGKPnfcYRxx8d+UBA3bt3Z83q9nw67n8/fPw4Yy3jPqzP4YcfGWnsyFWw+6264IqIiMiOSHgLqLsPBYYChC2gV7v7qWY2CjiBICk9E3g93GR0+PiLcPkHXs1G++jYsSPXP/h37rzhNnzpRtbHNnBA/99w1bVXJyT+HQ/dzj+uvYWnPnmI+lafrHYNue+uu8nOzo48dp++fbjktjN56PYRpK5PZ0NqPidd/jtOPfOUyGOnpKTw6HMPcf0fbyJ32lgw2Gmv9oy45yFSUqL/beW0s0+lYH0Bo558hIyNDSjKKOTS2y5k//33jzx206ZNuefJu/n7n/7B2oVrKaSQfQ/eh2H/GBZ5bIBrb/4r99x6F/e+/SQNLJ3U7Hrc8NDf6dChQ+Sxd9ttN34/bCj/vOVu6hU462IbOOy4/lx61e8jj21m3D7sMYbdfg1PjpxM/XpGgwYdGXbbXZG3vkbN0Si4IiIiEj1LZi4Xl4AeY2Y7879pWL4CTnP3DWaWATwN7EPQ8jnY3Wdva785OTmemxv99Y+luTtr164lPT09YVOgxCssLKSwsJBGjRolPHYsFmPt2rU0bNgwKdNRrF+/HoAGDRokPHZxcTHr1q2jUaNGCUl8S1u7di3169dP2mtuw4YNNGrUKGFT35Rwd9asWUNmZiZpaYmfUWrDhg0UFxcnbOqZspjZJHevkrl3umbu5Hd3H1qhbY+bcnGV1UMqz8x2BV6MK9oZuMHd74tbpx/BD70/hUWvuvvN29t3sj5fZcdNevttelVBj7AX3nqLwUcfXbm6LF1Kr/79K12XqjqmqqqPiPzPjnwnSdo8oADu/hHwUXh/NrBFv0l3LwCi789ZBcyMrKys7a8YkWQlIRC0RjZunLwBG5OReJZITU1N6rEn4weHEsl8zZlZUs97enr69leqYdQCWju4+w/A3rBpzu1fgP+Useo4dz8mkXUTERFJagIqIiLVhINHO2uUJMdhwCx3j35uKhERkXJQAioiIqSkp5G1S7OKbTytausiVWow8PxWlvU1sykEg/5d7e7fJq5aIiJSVykBFRERYhuKyJu55bRGUnOZWX1gIOHAf6VMBnZy97VmNgB4DehWxnpJneZMRERqn2RMwyIiItWMY8S8Yjepto4GJrv74tIL3D3P3deG98cA9cysRVk7SeY0ZyIiUvuoBVRERAANQlQLDWEr3W/NrA2w2N3dzHoT/CC9PJGVExGRukkJqIiIAEpAaxMzywSOAC6MK7sIwN2HE8yrfbGZFQHrCaY4q1ZzbIuISO2kBFRERIJRcJWA1hrung80L1U2PO7+g8CDia6XiIiIElAREcFB13OKiIhI5JSAiogIqelpNO6aXbGNZ1RtXURERKT2UgIqIiIUbyhi9Y+rkl0NERERqeWUgIqICGC6BlREIjFr+vQq2c9P06fTS1MBidR4SkBFRASIdhAiM+sP3A+kAo+5+7Ay1jkJuJHgktQp7n5KWN4JeAzoGC4b4O5zzGwckBVu3gqY4O7HRXYQIlIhsXXrqiRx/DE3twpqIyLJpgRURERwh1hEk3CYWSrwEMG0IPOBiWY22t2/i1unGzAUOMDdV5pZq7hdPAXc4u7vmlkjIBbU2Q+K2/4V4PVojkBERESqSkqyKyAiItWDu1XoVg69gZnuPtvdC4EXgEGl1jkfeMjdVwZ18SUAZrY7kObu74bla8MpRjYxsyzgUOC1yhy/iIiIRE8JqIiIAJVKQFuYWW7c7YJSu24PzIt7PD8si9cd6G5mn5nZl2GX3ZLyVWb2qpl9ZWZ3hi2q8Y4H3nf3vKo5EyIiIhIVdcEVEZFwHtAKb77M3XO2sbysZtLS0dKAbkA/oAMwzsz2DMsPAvYB5gIvAmcBI+O2HUJwjaiIiIhUc0pARUSEtPQ0srtVcB7Qn7e7xnyCAYRKdAAWlLHOl+6+EfjJzH4gSEjnA1+5+2wAM3sN6EOYgJpZc4IuvsdXrPIiIiKSSEpARUSEog3FrJyxOqrdTwS6mVkX4BdgMHBKqXVeI2jJ/LeZtSDoejsbWAU0NbOW7r6U4FrP+KEwTwTecPeCqCovIiIiVUfXgIqICHh0gxC5exFwGTAWmA685O7fmtnNZjYwXG0ssNzMvgM+BP7k7svdvRi4GnjfzKYSdOf9V9zuBwPPV9l5EBERkUipBVRERIBgKpbo9u1jgDGlym6Iu+/AVeGt9LbvAj23st9+VVpRERERiZRaQKuQu7Nu3TqKioqSEn/jxo3k5+dvf8UIuDtr164lFoslJf6GDRvYsGFDUmInW35+Phs3bkxK7KKioqS+5tatW0dxcXFS4tc2DnisYjcRERGR8lILaBWZlDuJu/82jKJV6ymIFXLggEP4wzVXU69evchj5+fnc9v1t5H78STSqE+TdlnccNf1dO/ePfLYAG+98RYPDhtOSn4aG+tt4ORzTuCsC87ErFzzA1bKkiVLuOGPNzHn22CGhy49OnHTnTfQqlWr7WxZ882YMYO///lm1izIY6NvpFe/HK65aSiZmZmRxy4sLOTeYXfz6VsfkmH1SWvagD/dPJR9e+0beWyAzz77hEeG30x6+hrWrDH69TuBiy68ipQU/aZWGeWc01NERESkwpSAVoH58+fzj8uv4/z2A2jWrAkxj/HuBxO4o/A2rvv7DdvfQSX95fJryPq6Gec0uQQzY8myxVx5xlU8P/ZZmjRpEmns8ePH89A1IxnY6GwyGjSgKLaRd//5GhmZ6Qw5fUiksWOxGBeecgl7L+1H30bBZWQ/fT2Di0+7jFFvv1Crk5FVq1Zx1ZlXckKDQbRq1Qp3Z+K4SQy94hru/9cDkce//cZbKf5sKX/ufCoplsLy9au46dJrefiVkbRvX3p6x6r1ww8/8Oi/ruLWu1rQrFlrioudkSNGMfxRuOTiqyONXas5xJSAioiISMRq7zf0BBr19AsckbUPzRoEyV6KpXBk+/3Jff/zyLsnLly4kF+mLGDf7P02tTi2atCa3Qt7MPo//400NsDI+/9Nv/RjyUhtAEBaSj36NTqW50a8FHns8ePHk7W0JZ0bddtU1qVRdzIXN2PixImRx0+m/772X/aO9aBVZtDSa2b0bpHDL9/MZ9GiRZHGXrduHZM+HM/h7fYnxYK3kOYNsjms0T6MeuaFSGMDvPjSCC64JJNmzeoDkJpqnHtBKz78cJS641ZCakYqTbs3qdBNREREpLzUAloFFs5dwP6ZbTcrMzOapmWxatWqSLtELl26lKbWfIvy5qktWTi39DR7VW/RL4vpm755d9f01AwK86O/JnHJkiVkbWgKjTYvb1zYjMWLF0ceP5kWzPmF5vW2fN6bpTRl6dKltGnTJrLYq1atomm9rC26WLdu0JzJCXjNLVkyjw4dG2xWlppqZGVBQUEBDRs2jLwOtVFxQTErfohsGhYRERERQC2gVaLXQb2ZumrWZmUFRRtYYeto3bp1pLG7du3KAp9PcWzzlp+ZxTPIOSAn0tgAOQfsy495325WtqxgMW12iv4azJ49e/JLxiw8buhOd2de+kx69ixzwMxaI+fA/fixcOZmZcWxYhbEFrLLLrtEGrtNmzYs9zUUFG0+6NO0vFnkHLx/pLEB9t7rYD7/NG+zshUrCikszErI9a+1lRN0wa3ITURERKS8lIBWgYHHD+LHxit4b94EVhTkMXPlPB756XUu+vPlpKamRho7MzOTUy8dwqgVz/LLunms3LCSj1a8h3fbyG/6/SbS2AAXXnE+Xzf+hKmrc1m7cQ0/5n3HO/4SV9/0h8hjd+nShZ5H/op3815l+YYlLCtYzDt5r7DPUXvQuXPnyOMnU79D+lHQZSPvLf6AFQUrmbd2Ps8sepEhF58aeRKWmprKRX+5nEd/fo2ZK+eysiCP934Zz+wmyzl20MDt76CSTjrpDN58vSGv/2cpy5Zt4KvJq/jrX5Zx8UU3JGTgq9rMvWI3ERERkfJSF9wq0KBBA0Y8/wQvPfcir78/jhZdW/HX24bRo0ePhMQ/7ezT6L5Hd1547EXWrF7DYQMP43cnHh958gvQunVrnhr9OM+MfJbciW/TpXtnRlz4EDvttFPksQFuHHYDY/uNZfTzbwJwxin/x5FHHZmQ2MmUmprK8Kcf5ZWXXuHDNz+gcZPG/PGcP9G7d++ExB9wzAA6dOrACyOfYdnimRxwxm/405CTyMjIiDx248aNeeThV3nppae469ZxtG7dg79ee0HCRn2uvUyj4IqIiEjklIBWkYYNG3L2+edw9vnnJCV+7969E5Z8lNaiRQuu/MsVSYmdkpLC0QOO5ugBRyclfjLVr1+fIacNYchp0Y42vDU9e/ak5/13JCV248aNOe+8y4DLkhK/NnKHmFozRUREJGJKQEVEBFB3WhEREYmeElARESEtI5Vmu2VXbOPPq7YuIiIiUnspARUREYoKilk2XdOwiIiISLSUgIqICKAuuCIiIhI9JaAiIoKjBFRERESipwRUREQAiKFpWERERCRaSkBFRARcLaAiIiISPSWgIiKCA7FkV0KqlJnNAdYAxUCRu+eUWm7A/cAAIB84y90nJ7qeIiJStygBFRERAFxdcGujQ9x92VaWHQ10C2/7A4+Ef0VERCKjBFREREjLSKXFbo0rtvGEqq2LJMwg4Cl3d+BLM8s2s7buvjDZFRMRkdpLCaiIiFBUUMzS6XnJroZULQfeMTMHHnX3EaWWtwfmxT2eH5ZtloCa2QXABQCdOnWKrrYiIlInKAEVERFA14DWQge4+wIzawW8a2bfu/snccvL6nO9xVBUYeI6AiAnJ0dDVYmISKWkJLsCIiKSfCXzgFbkJtWTuy8I/y4B/gP0LrXKfKBj3OMOwILE1E5EROoqJaAiIgKESWgFblL9mFlDM8squQ8cCUwrtdpo4AwL9AFW6/pPERGJmrrgiogIADFlk7VJa+A/wUwrpAHPufvbZnYRgLsPB8YQTMEyk2AalrOTVFcREalDlICKiAgQbWummfUnmHMyFXjM3YdtZb0TgFHAfu6eG1feCfgOuNHd74orTwVygV/c/ZgID6FGcffZwF5llA+Pu+/ApYmsl4iIiBJQEREJpmH5VQWnYcnd9uIwSXwIOILgusOJZjba3b8rtV4W8HtgfBm7uRd4q4zyK4DpQAUrLyIiIomkBFRERNhYUMyS6KZh6Q3MDFvlMLMXCOag/K7Uen8H7gCuji80s+OA2cC6UuUdgN8CtwBXRVJzERERqVIahEhERIBKDULUwsxy424XlNr11uab3MTM9gE6uvsbpcobAn8BbiqjyvcBf0YzyIiIiNQYagEVERGgUlncMnfP2cbybc43aWYpBF1szypjvZuAe919bTigTsk2xwBL3H2SmfWrSKVFREQk8ZSAiojIpnlAI7K9+SazgD2Bj8Iksw0w2swGAvsDJ5jZHUA2EDOzAoIW1IFmNgDIABqb2TPuflpkRyEiIiKVlvAE1Mw6Ak8RfMGIASPc/X4zawa8CHQG5gAnuftKC76N3E8wVHw+cJa7T050vctjzpw55E6cSLPmzTnwwAOpX79+wmKvX7+ecePGsWbNGvr27Uu7du0SFtvdmTZtGt9P/56OnTrSu3dvUlIS17t71apVfPLJJwAcfPDBZGdnJyx2LBZjwoQJzJs7j1/t/iv22GMP4ltporZgwQK++OILsrKyOOigg2jQoEHCYkvtE2E/1olANzPrAvwCDAZOKVno7quBFiWPzewj4OpwFNyD4spvBNa6+4Nh0dCwvF+4vpJPERGRai4ZLaBFwB/dfXI44uEkM3uXoOvV++4+zMyuAa4huO7naKBbeNsfeCT8W224O3ffchvfvDuOnKy2TC0u4OHb7uSufz1C586dI48/depUrrv4z/yKTjQkg2dvf5zfnn085158XuSxCwsLueqiq1j5zWo6bOzA2HrvcH/bBxj+zCM0adIk8vhj3xrLfdffz27Fu+M4j6QO5w//uJIj+x8ZeexVq1Zx8WkXkbW0IS2LWvDftNdpsmdT7hl+D/Xq1Ys8/mMP/4sx/36Nvet3JZ8CHuRebh1+J3vuuWfksaV2iqoB1N2LzOwyYCzBNCyPu/u3ZnYzkOvuoyMKLSIiItVMwhNQd18ILAzvrzGz6QRdqQYB/cLVngQ+IkhABwFPhfOVfWlm2WbWNtxPtfD5558z54PxXLvPUZtav35etZSbrr6GJ15+IdLYsViMv11xLec0O4bmDYKWv994Dg//exQHHnoQu+66a6Txn3/meVK+qscJLU/aVDZ10TfcdfNd/P3uv0caOy8vj/uuv58zm5xDemo6AH2K+3LvX++jz6/70LhxtLMy3HXzXfRcvjs9W/TYVPbeNx/w/DPPc8bZZ0Qa+/vvv+e9J9/gis6DSbGgtbnP+lXc8Ptrefm91xLaAi21Q1pGKq0qOg3LpO2v4u5jgDGlym7Yyrr9tlJ+41bKPyL4zBAREZFqLqnfUs2sM7APwZxvrUuSyvBvq3C17Y6emGxjX32do9r/arOulztltyS2PI9ly5ZFGnvGjBm0KGy8KfkESLEUDsjswdjRZU2ZV7XefGkMfZr23axsz+we5I7bzsSAVeDTTz+le/Gum5JPgPTUdLrHduXzzz+PPP6kT3Pp0XTz1sZfN+vLmJfejDz22NFvcUBmj03JJ0DzBtm02JjFjz/+GHl8qX02FhSzaHpehW4iIiIi5ZW0QYjMrBHwCnClu+dt47q5bY6eGLe/C4ALADp16lRV1SyXlJQUvIzRO2J45C1RKSkpeBkd52JAWmr0vy+kmG0R3/Gyn7Wqjp2SUmaXwUSc9xKOY3EH6x4jJQHn3VKMYt/yir1ijyX0GlSpXTSXiYjUBbOmT6+S/cydN49OHTtuf8UE7ad+djY9+vSp9H5EopaUBNTM6hEkn8+6+6th8eKSrrVm1hZYEpZvb/REANx9BDACICcnJ7qxHMsw4MTf8fhfbqZbizabWqRmLl9EeutmNGvWLNLY3bp1Y2WDfBbnL6d1ZnMAimJFfJY/hWGD7o80NsDAIQP59N5xHNHyqE1lX6/8ir5H9N3GVlXjwAMP5P60+9m/qA+ZaZkA5BflMzN1BgcccEvk8fc/pA9ff/IN+zbbe1PZuBWfc+yVAyOPPeC4Yxj60h/YK7YbaSmpACxet4y8zAK6desWeXypnRL6xikikiSxdevo1bJlpffzY24uvfbdt9rsZ9LSpZXeh0giJGMUXANGAtPd/Z64RaOBM4Fh4d/X48ovM7MXCAYfWl2drv8E6N27N7mDjuTmV99gn8ZtWVFUwE+2nntHDo88tplx60N38Ofz/0CXVW3IjNVnWtEcBl96Orvsskvk8U8+9WS+mvA1z3/5DB2KO7EsbSkpneHB6x7c7raV1ahRI4beOZTb/jSMrrGugDEz9UeuvWsoDRs2jDz+H//6Ry4/+zJmz/uJlrEWzLX57PTrzpw05KTtb1xJXbt25bhLT+b+h59lj3pdWG8bmJ2yiDsfu08toFIhjlpARUREJHrJaAE9ADgd7JU7XQAAIABJREFUmGpmX4dl1xIkni+Z2bnAXODEcNkYgilYZhJMw3J2YqtbPpdc+Xt+N/gkvvrqK5o1a8Z+++2XsG6g3bt3Z9R7rzF+/HjWrVvHH/fbL/KW1xKpqanc+eAdzJ49m++//54OHTrQo0ePhCVBB/c7mJyPc/jiiy8A6Nu3L5mZmQmJnZWVxROj/s3UqVOZP38+u+22GzvvvHNCYgOceuZpHH3sACZMmECjRo3Yf//9EzL6rtReZXXnFxEREalKyRgF91O2foXgYWWs78ClkVaqirRp04ajjz46KbHr1avHgQcemJTYADvvvHNCk694mZmZHHbYFi+dhDAzevbsSc+ePZMSv1mzZvTv3z8psaX2UfopIiIiUUvaIEQiIlJ91MtIpU2E07CIiIiIgBJQERFB14CKiIhIYigBFRERNhYUs1BzeoqIiEjElICKiAgAbroKVERERKKlBFRERAB1wRUREZHoKQEVERFdAyoiIiIJoQRUREQAzQMqIiIi0VMCKiIigFpARUREJHpKQEVEhHoZKbSr6DyguVVbFxEREam9lICKiAgbC4r5ZfrqZFdDREREajkloCIiokGIREREJCGUgIqICABuya6BiIiI1HZKQEVEBICYRsEVERGRiCkBFRERdcEVERGRhEhJdgVERKR68Ar+k+rFzDqa2YdmNt3MvjWzK8pYp5+ZrTazr8PbDcmoq4iI1D1qARUREeplpNL+V00qtvHEqq2LVFoR8Ed3n2xmWcAkM3vX3b8rtd44dz8mCfUTEZE6TAmoiIiwsaCYeZqGpVZw94XAwvD+GjObDrQHSiegIiIiCacuuCIigqMuuLWRmXUG9gHGl7G4r5lNMbO3zGyPbezjAjPLNbPcpUuXRlRTERGpK5SAiogIADGr2K08zKy/mf1gZjPN7Joyll9lZt+Z2Tdm9r6Z7RSW721mX4TXMn5jZifHbfNsuM9pZva4mdWrqnNRG5hZI+AV4Ep3zyu1eDKwk7vvBfwTeG1r+3H3Ee6e4+45LVu2jK7CIiJSJygBFRERILoWUDNLBR4CjgZ2B4aY2e6lVvsKyHH3nsDLwB1heT5whrvvAfQH7jOz7HDZs8BuQA+gAXBe5c5A7REm468Az7r7q6WXu3ueu68N748B6plZiwRXU0RE6iAloCIiUuHks5xdcHsDM919trsXAi8AgzaL7/6hu+eHD78EOoTlM9z9x/D+AmAJ0DJ8PMZDwISSbeo6MzNgJDDd3e/ZyjptwvUws94E3weWJ66WIiJSV2kQoiqSl5fHs8+NJDf3fZo2bcWJJ1zI/vvvn7D4H37wAf95+lnW5K3h4P5HMvi0U2nQoEFCYi9YsIAnHx3Jd199Q6ddunDmxefRvXv3hMSOxWL855VXeWvUa5gZR594HMf97nhSUhLz28qMGTN48pF/MW/2HHbfZy/OvPBc2rZtm5DYyTZ+/HhGvTyclSuXst9+h3PKkHNo3LhxsqsllVCJeUBbmFlu3OMR7j4i7nF7YF7c4/nAtt4gzwXeKl0YJkr1gVmlyusBpwNbTDdSRx1AcD6mmtnXYdm1QCcAdx8OnABcbGZFwHpgcJjIi4iIREoJaBVYt24dl1x6Ascct4Zb72rKooU/8+hDl7B48VAGDjwh8viPD3+Ur14ZzeBuPWncdCc+fOsjLn33PUY89wxpadE+xb/88guXn3IOx2bvyWGtDmLOvMVce9ZlXPvP29i3V69IYwNcd9WfYepCTuuwNwBvP/Iqkz+fwD/uuT3y2JNycxl2xVAGd8jh/9r+mmnfzOWyIWfxz+eeoF27dpHHT6bXXx/F2HeHcdFl2bRqnc6H77/IJZeOYcSjr5GZmZns6kkF1M9IpUNFp2GZwDJ3z9nGGmVdKVpmsmNmpwE5wG9KlbcFngbOdPfSufLDwCfuPq78la693P1Tyj7n8es8CDyYmBqJiIj8jxLQKvD66y9z6BF5HDOwFQC7dE3j5tsyuOS8exgw4LhIk8C1a9cy5vkXuaPvkaRY0Oo3cNeerJg2kQ/ef58jjzoqstgAI//5CMc17cnebXYBYLcWHbmwQRYP3nY3j7/8XKSxZ86cyZKvf+SK3Y7eVHZKt4O4d9IYZs2axS677BJp/AeH3c2l3fvRqmFwOVpOu66kWgojHxzO9bfeHGnsZCoqKuLZ5+7lkZFtSE9PBeDYQS1Zt3YJo0e/wuDBpye5hlIRhQXFzJ2+Kqrdzwc6xj3uACwovZKZHQ5cB/zG3TfElTcG3gT+6u5fltrmbwRdci+MoN4iIiJSxXQNaBWY9u3n7Ld/w83KMjJSad8BlixZEmns2bNn071x803JZ4m9m7VhWu7kSGMDTJ08hZ6tu2xW1rphNisXRT9U/7Rp09itXqstyndPb823334befzVi5ZtSj5L7NWmM1MnRX/ek2nx4sV06Mim5LNE7z4Nmfbt50mqlVSWV+JWDhOBbmbWxczqA4OB0fErmNk+wKPAQHdfEldeH/gP8JS7jyq1zXnAUcCQMlpFRUREpBpSC2gVaN9uF36aPZUuO/8vCY3FnIULi8jOzt7GlpXXpk0bfskvPbo+zF27mnZd+kYaG6Bdx/bMz1tGpyb/SwTXFRZQLzMj+tjt2vFxbM0W5QuL19AvAddhpjaoT/7GDWTWS99UNm/1Mtp36riNrWq+pk2bsmBBEe5OOIYJAD/NLqBt22hbnSVaMYvmEkB3LzKzy4CxQCrwuLt/a2Y3A7nuPhq4E2gEjApfV3PdfSBwEnAw0NzMzgp3eZa7fw0MB34Gvgi3edXda2/3AxGRbZg1fXqV7Kd+djY9+vSpkn1V1tQvv6RwVeV751SnYxIloFXid787nSuveoVu3fPp2CmToqIYTz2xlN45x0R+PVyrVq1ou+fuvPnjtxzd9VekWAqzli/hk1WLeOLYYyONDXDGJedz5+XXcWn3I2iS0ZD1Gwt5auYnDLn8zMhj5+Tk8ECjYiYvmsk+rYPEZ/KiWSxpVEyvBFx/OuT8s3jykec5q/tBNKhXn1UF63j6py/580PRX3+aTJmZmezXawBPjHyLM85qSVpaCnN/zue5p4u4/97Tkl09qYQomxDDqT7GlCq7Ie7+4VvZ7hngma0s02eYiEgotm4dvapgrt5JS6PvxVZehatW1bpjEiWgVaJt27b87fonuO/Ov7I6bx5FG1M57LCTuPSKKxMS/6Y7hvHAnXdx1TtjSQXadOnMXSNHkJWVFXnsXr16cdEtf+GhO+5nY14+pKcx5OKzOO6E/4s8dkpKCg888Sh33HgLr+a+DMAe++3NA397NCGj4B5/4gkUFxdz62NPwoYi0ps04sJbhrL33ntHHjvZrrzyekb8K4vzzhhFWr1ispt04m/XP0CbNm2SXTWpoKA7rQZBFRERkWgpAa0iu+++O48Of5VYLIaZbdY1MWrp6en86a/XcfV11+LuCZuCpES/Qw6h3yGHEIvFEh67WbNmDHvgbmKxoO0mkfHNjBOHDObEIYOTcuzJlJqaysUX/ZGLLrwqKa85iYITUwIqIiIiEVMCWsWS+UU80Ylvack89mQnQMmOnyzJfs1J1dIoPiIiIhI1JaAiIkL9jDR22q2Cg6ZNqNq6iIiISO2lBFRERNhQUMRP369MdjVERESkllMCKiIigAYhEhERkegpARUREUDXgIqIiEj0lICKiAhuEDO1gIqIiEi0lICKiAiAOuCKiIhI5JSAiogIgOYBFRERkcgpARUREepnpNKlgtOwjB9fxZURERGRWksJqIiIsKGgiNmahkVEREQipgRUREQAdcEVERGR6CkBFRERQAmoiIiIRE8JqIiI4CgBFRERkegpARUREUAJqIiIiERPCaiIiAQtoJbsWoiIiEhtpwRURERIz0hl592aVmjbaV9WcWVERESk1lICKiIibCgoYub3K5JdDREREanllICKiAiga0BFREQkekpARUQEB4qVgIqIiEjElICKiAigFlARERGJnhJQERHRPKAiIiKSEEpARUQEcIotluxKiIiISC2XkuwKlJeZ9TezH8xsppldk+z6iIjUJiUtoBW5lcf23sPNLN3MXgyXjzezznHLhoblP5jZUeXdZ11WmfMtIiISpRrRAmpmqcBDwBHAfGCimY129++SWzMRkdohPSONrrs1r9C287/Y9vJyvoefC6x0965mNhi4HTjZzHYHBgN7AO2A98yse7iNPhfKUJnznfjaiohIXVMjElCgNzDT3WcDmNkLwCCgzn/REBGpChsKivjh+2VR7b487+GDgBvD+y8DD5qZheUvuPsG4Cczmxnuj3Lss66q8Pl2d10ILCIikbKa8FljZicA/d39vPDx6cD+7n5Z3DoXABeED/cEpiW8otVDCyCyb5HVnI69bqrLx76ru2dVxY7M7G2Cc1kRGUBB3OMR7j4ibt/leQ+fFq4zP3w8C9ifIEn60t2fCctHAm+Fm21zn3VVZc63u2/xf6nU5+uuwA+VrGJN/D+rOkevptUXVOdEqGn1hZpX56qq707u3rI8K9aUFlAro2yzzDn8sjMCwMxy3T0nERWrbnTsOva6pq4fe1Xty937V9W+yrDd9/BtrLO18rLGMKj+v6gmRmXO95aFcZ+vVaEm/p9VnaNX0+oLqnMi1LT6Qs2rczLqW1MGIZoPdIx73AFYkKS6iIjIjinPe/imdcwsDWgCrNjGtvpc2LrKnG8REZFI1ZQEdCLQzcy6mFl9ggEpRie5TiIiUj7leQ8fDZwZ3j8B+CC8HnE0MDgctbUL0A2YUM591lWVOd8iIiKRqhFdcN29yMwuA8YCqcDj7v7tNjapsq5CNZCOvW7SsddNNeLYt/YebmY3A7nuPhoYCTwdDjK0giBpIlzvJYIBdIqAS929GGAHPxfqjMqc7wSpEa/bUlTn6NW0+oLqnAg1rb5Q8+qc8PrWiEGIREREREREpOarKV1wRUREREREpIZTAioiIiIiIiIJUesSUDPrb2Y/mNlMM7sm2fWpSmbW0cw+NLPpZvatmV0Rljczs3fN7Mfwb9Ow3MzsgfBcfGNm+yb3CCrPzFLN7CszeyN83MXMxofH/mI44AbhgCUvhsc+3sw6J7PelWVm2Wb2spl9Hz7/fevK825mfwhf79PM7Hkzy6itz7uZPW5mS8I5GkvKdvh5NrMzw/V/NLMzy4olEoXtfQZv6/+omQ0Ny38ws6OqUZ2vMrPvwv9n75vZTnHLis3s6/CWkEGwylHfs8xsaVy9zotblpT3hnLU+d64+s4ws1Vxy5Jxjrd4Ly61vNq9/5ajzqeGdf3GzD43s73ils0xs6nhOa6y6b0qWd9+ZrY67rm/IW5ZUr7rl6POf4qr77TwtdssXJaMc1xm3lBqneS8lt291twIBluYBewM1AemALsnu15VeHxtgX3D+1nADGB34A7gmrD8GuD28P4AggnbDegDjE/2MVTBObgKeA54I3z8EjA4vD8cuDi8fwkwPLw/GHgx2XWv5HE/CZwX3q8PZNeF5x1oD/wENIh7vs+qrc87cDCwLzAtrmyHnmegGTA7/Ns0vN802cemW+2/leczeGv/R8PPsilAOtAl3E9qNanzIUBmeP/i+PcVYG01PMdnAQ+WsW1S3hvKU+dS619OMHBWUs5xGHOL9+JSy6vd+2856vzrkroARxP33QCYA7SoZue4H+F3vcq8nhJZ51LrHkswungyz3GZeUOpdZLyWq5tLaC9gZnuPtvdC4EXgEFJrlOVcfeF7j45vL8GmE7wBX0QQYJC+Pe48P4g4CkPfAlkm1nbBFe7yphZB+C3wGPhYwMOBV4OVyl97CXn5GXgsHD9GsfMGhO86Y0EcPdCd19FHXneCUbrbmDBXIWZwEJq6fPu7p+w5VyMO/o8HwW86+4r3H0l8C7QP/rai5TrM3hr/0cHAS+4+wZ3/wmYGe4v6XV29w/dPT98+CXBvKrJUpnvOcl6b9jROg8Bnk9AvbZqK+/F8ard++/26uzun4d1guS/jstzjrcmad/1d7DO1eF1vLW8IV5SXsu1LQFtD8yLezyfLU90rWBBt6V9gPFAa3dfCMGLDWgVrlbbzsd9wJ+BWPi4ObDK3YvCx/HHt+nYw+Wrw/Vrop2BpcATFnQ/fszMGlIHnnd3/wW4C5hLkHiuBiZRN573Ejv6PNea519qnPK89rb2fzRZr9sdjXsuQWtBiQwzyzWzL83suK1tVIXKW9//C7vTvWxmHXdw26pW7rhh9+YuwAdxxYk+x+VR099/S7+OHXjHzCaZ2QVJqlNZ+prZFDN7y8z2CMuq/Tk2s0yCZO2VuOKknuNSeUO8pLyWa8Q8oDugrJaOWjfPjJk1InhRX+nuedto4Kk158PMjgGWuPskM+tXUlzGql6OZTVNGkGXj8vdfbyZ3U/QFXNras2xW3C94yCCLySrgFEEXYdKq43P+/Zs7Vjr0jmQ6qU8r73q9rotd1wzOw3IAX4TV9zJ3ReY2c7AB2Y21d1nRVDPTdUoo6x0ff8LPO/uG8zsIoIW50PLuW0UdiTuYOBlD+f6DSX6HJdHdXsdl5uZHUKQgB4YV3xAeI5bAe+a2fdha18yTQZ2cve1ZjYAeA3oRg04xwTdbz9z9/jW0qSd49J5Q+nFZWwS+Wu5trWAzgc6xj3uACxIUl0iYWb1CF5Ez7r7q2Hx4pIuluHfJWF5bTofBwADzWwOQXeLQwlaRLPDrpmw+fFtOvZweRMq1tWjOpgPzHf3kl+tXiZISOvC83448JO7L3X3jcCrBNex1IXnvcSOPs+16fmXmqU8r72t/R9N1uu2XHHN7HDgOmCgu28oKXf3BeHf2cBHBC0MUdpufd19eVwd/wX0Ku+2EdmRuIMp1W0xCee4PGrk+6+Z9SS4jGmQuy8vKY87x0uA/5CY7u/b5O557r42vD8GqGdmLajm5zi0rddxQs/xVvKGeEl5Lde2BHQi0M2CETLrE7wAEjJiWiKE18mMBKa7+z1xi0YDJaNTnQm8Hld+RjjCVR9gdUlXvprG3Ye6ewd370zwvH7g7qcCHwInhKuVPvaSc3JCuH51+4WsXNx9ETDPzHYNiw4DvqMOPO8EXW/7mFlm+PovOfZa/7zH2dHneSxwpJk1DVuQjwzLRKJWns/grf0fHQ0MtmCU3C4ELR0TqkOdzWwf4FGC5HNJXHlTM0sP77cg+KH0u2pQ3/hr/gcSXPcFyXtvKNd3s/AzrinwRVxZMs5xedS4918z60TwI+7p7j4jrryhmWWV3Ceoc5mjvCaSmbUJP/cxs94EOctyqvl3fTNrQtBL4vW4sqSc423kDfGS81r2BI7GlIgbwWhOMwhGyLou2fWp4mM7kKD5+xvg6/A2gOD6mfeBH8O/zcL1DXgoPBdTgZxkH0MVnYd+/G8U3J0JvqTMJOiemR6WZ4SPZ4bLd052vSt5zHsDueFz/xrBh3SdeN6Bm4DvCd6snyYYJbNWPu8Ev5guBDYS/Pp4bkWeZ+Cc8BzMBM5O9nHpVnduZX0GAzcTJG/b/D9K0MI4C/gBOLoa1fk9YHHc5+7osPzX4f+9KeHfc6tJfW8Dvg3r9SGwW9y2SXlv2F6dw8c3AsNKbZesc1zWe/FFwEXh8mr3/luOOj8GrIx7HeeG5TuH53dK+LpJyHfnctT3srjX8ZfAr7f1eqoOdQ7XOYtgQLX47ZJ1jreWNyT9tWxhABEREREREZFI1bYuuCIiIiIiIlJNKQEVERERERGRhFACKiIiIiIiIgmhBFREREREREQSQgmoiIiIiIiIJIQSUJFSzKyjmf1kZs3Cx03DxzuVsW4DM/vYzFJ3YP+XmdnZVVlnERER2TYzu9fMrox7PNbMHot7fLeZXbWN7T8vR4w54Zylpcv7mdmvK1JvkdpGCahIKe4+D3gEGBYWDQNGuPvPZax+DvCquxfvQIjHgd9XrpYiIiKygz4nmFsUM0sBWgB7xC3/NfDZ1jZ298okkP1KYovUdUpARcp2L9An/KX0QODurax3KvA6bPp182Mze8nMZpjZMDM71cwmmNlUM9sFwN3zgTlm1jsRByIiIiJAkFyWJIF7ANOANWFPp3TgV8BXZvYnM5toZt+Y2U0lG5vZ2vBvipk9bGbfmtkbZjbGzE6Ii3O5mU0OP/t3M7POwEXAH8zsazM7yMxONLNpZjbFzD5JwLGLVBtpya6ASHXk7hvN7E/A28CR7l5Yeh0zqw/s7O5z4or3IvgAWwHMBh5z995mdgVwOVDS9ScXOAiYEN1RiIiISAl3X2BmRWbWiSAR/QJoD/QFVgPfELRUdgN6AwaMNrOD3T0+Sfwd0BnoAbQCphP0biqxzN33NbNLgKvd/TwzGw6sdfe7AMxsKnCUu/9iZtmRHbRINaQWUJGtOxpYCOy5leUtgFWlyia6+0J33wDMAt4Jy6cSfFiVWAK0q7qqioiISDmUtIKWJKBfxD3+HDgyvH0FTAZ2I0hI4x0IjHL3mLsvAj4stfzV8O8kNv/sL12Pf5vZ+UC5x5EQqQ2UgIqUwcz2Bo4A+hB0mWlbxmrrgYxSZRvi7sfiHsfYvMdBRri9iIiIJE7JdaA9CLrgfknQAlpy/acBt7n73uGtq7uPLLUP206Mks/+YrbS29DdLwL+CnQEvjaz5hU5GJGaSAmoSClmZgSDEF3p7nOBO4G7Sq/n7iuBVDMrnYSWR3eCDz4RERFJnM+AY4AV7l7s7iuAbIIk9AtgLHCOmTUCMLP2Ztaq1D4+Bf4vvBa0NUG33e1ZA2SVPDCzXdx9vLvfACwjSERF6gQloCJbOh+Y6+7vho8fBnYzs9+Use47BF1xdtQBwHsVrJ+IiIhUzFSCS2i+LFW22t2Xufs7wHPAF+F1mi8TlziGXgHmE/yQ/CgwnuAa0m35L3B8ySBEwJ3hIEXTgE+AKZU8LpEaw9w92XUQqbHMbB/gKnc/PcptREREpPows0buvjbsOjsBOCC8HlREtkOj4IpUgrt/ZWYfmlnqDswF2gK4Psp6iYiISKTeCEevrQ/8XcmnSPmpBVREREREREQSQteAioiIiIiISEIoARUREREREZGEUAIqIiIiIiIiCaEEVERERERERBJCCaiIiIiIiIgkhBJQERERERERSQgloCIiIiIiIpIQSkBFREREREQkIZSAisj/s3fn4ZKV5dn2z0saHIDQIq0CMmhUjEMk0CLGCaMgcqCoUQMaxRFNNMZX88UhRg0Z3vj6qTExCUFFnECcUKIoEif0iyjdBASyUUZf2kZohmZoNW3D/f1Ra2uxqd3D7uqnqrrO33HU0avWetaqe9cu9s1Va5IkSZKaMIBKkiRJkpowgEqSJEmSmjCASiOU5C1JPjjqOraEJF9OcvSo65AkTRd7qzTeUlWjrkGaWEmuBO4OPKCq1nTzXg78YVUdNMK6vgkcCKwDfgGcBby6qq4eUT0vBl5eVY8bxetLkiaHvXWj63kx9lZNIPeASptvEfCnoy5igNdU1Q7Ag4HFwHs3dQNJFg29KkmSNszeKm2lDKDS5nsX8GdJFg9amOR9Sa5KcnOS5Uke37fsHUk+3k1/Jclr5qx7fpJnd9MPSXJmkhuS/DDJ8zamuKq6Afgs8PBuOzsl+WiSVUl+nOStSe7SLXtxkv8vyXuT3AC8I8ldujE/TnJtt+5O3fi7Jfl4kuuTrE5yTpL7dMu+meTlSX4LOA54TJJbu3GPSnJNfxNO8vtJztu4t1yStJWzt9pbtZUygEqbbxnwTeDP5ll+DrAvsDNwEvDpJHcbMO4k4KjZJ0keCuwFfCnJ9sCZ3Zh7d+P+JcnDNlRckl2A3wf+q5v1T8BOwAOAJwIvAl7St8qjgcu71/lb4MXd40ndOjsA7+/GHt1taw/gXsCrgJ/3v35VzXTzv1tVO1TV4qo6B7geOLhv6B8CH9vQzyNJmgr2VnurtlIGUGk43gb8SZIlcxdU1cer6vqqWldV7wbuCuwzYBunAvsm2at7/gLgc1X1P8DhwJVV9eFuO+fS++b1Oeup6R+TrAbOB64GXp9kG+APgDdX1S1VdSXwbuCFfeutrKp/6l7n510d76mqy6vqVuDNwJHdN6y/pNccH1hVt1XV8qq6eWPeMOAj9BojSXYGnkrvfwIkSQJ7q71VWyUDqDQEVXUh8EXgTXOXJXlDkpkkN3VNaydglwHbuAX4EnBkN+tI4BPd9F7Ao7tDbFZ323kBcN/1lPXa7hvR3avqBVW1qnvd7YAf9437MbB73/Or5mxntwHjFwH3ofet6hnAJ5OsTPJ/kmy7npr6fRx4epIdgOcB3x7VhRwkSePH3mpv1dbJACoNz9uBV9DXcLpzUt5Irwncs6oWAzcBmWcbJwNHJXkMvSsAfqObfxXwra7pzT52qKo/2sQar6P3zepeffP2BH7S93zupbFXDhi/Drimqn5ZVX9VVQ8Ffpfet8kvGvC6d7rcdlX9BPgu8Cx63xJ7iJAkaS57q71VWxkDqDQkVXUpcArw2r7ZO9JrKKuARUneBvzGejZzOr2GdCxwSlXd3s3/IvDgJC9Msm33eFR3EYJNqfE24FPA3ybZsTsk6fX0vjGdz8nA/0py/+4b1b/raluX5ElJHtEdfnQzvQZ824BtXAPcL8l2c+Z/FPhz4BH0DpOSJOlX7K32Vm19DKDScB0LbN/3/Azgy8CP6B1e8wvufBjOr3TnpHwOeAp952x0hxAdQu/QoZXAT4F30jvnZVP9CbCG3sUQvtO9zgnrGX8CvW9QzwKu6H6GP+mW3Rf4DL0GOQN8i8EN9+vARcBPk1zXN/9Uev9TcOrsvd4kSZrD3mpv1VYkVXfaey9JzSS5DHhlVf3HqGuRJGlrYG/VOHMPqKSRSfL79M5h+fqoa5EkaWtgb9W4axJAk+yR5Bvd1couSvKn3fyd07v57yXdv/ecZ/2juzGXJDm6Rc2Stqwk3wT+FXh13/k4kjaSvVXSXPZWTYImh+Am2RXYtaqa1OJfAAAgAElEQVTOTbIjsBx4Jr0b8N5QVX+f5E30rmT2xjnr7kzvZsRL6X2bsxzYv6pu3OKFS5I0puytkqRJ1GQPaFVd3d3cd/aE7xl6l9M+gt4Nc+n+feaA1Z8KnFlVN3SN8Uzg0C1ftSRJ48veKkmaRM3PAU2yN/A7wPeA+8zeHLf7994DVtmdO17ZbAV3vLGvJElTzd4qSZoUi1q+WHefo88Cr6uqm5P57hd8x9UGzLvTccNJjgGOAdh+++33f8hDHrI5pUqStlLLly+/rqqWjLqOYdmSvbXbvv1VkrRem9JbmwXQJNvSa5CfqKrPdbOvSbJrVV3dncty7YBVVwAH9T2/H/DNuYOq6njgeIClS5fWsmXLhli9JGlrkeTHo65hWLZ0bwX7qyRpwzalt7a6Cm6ADwEzVfWevkWnAbNX3jsa+MKA1c8ADklyz+5Kfod08yRJmlr2VknSJGp1DuhjgRcCv5fkvO5xGPD3wMFJLgEO7p6TZGmSDwJU1Q3AXwPndI9ju3mSJE0ze6skaeI0uQ1Lax4iJEmaT5LlVbV01HVMIvurJGmQTemtza+CK0mSJEmaTgZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhOLRl2AJEmSNGkuOPts1q5evdnb2W7xYh5x4IFDqEiaDAZQSZIkaROtXb2a/Zcs2eztLF+1agjVSJPDQ3AlSZIkSU0YQCVJkiRJTRhAJUmSJElNGEAlSZIkSU0YQCVJkiRJTRhAJUmSJElNGEAlSZIkSU0YQCVJkiRJTRhAJUmSJElNGEAlSZIkSU0YQCVJkiRJTRhAJUmSJElNGEAlSZIkSU0YQCVJkiRJTRhAJUmSJElNGEAlSZIkSU0sGnUBkra8C84+m7WrV2/2drZbvJhHHHjgECqSJEnSNDKASlNg7erV7L9kyWZvZ/mqVUOoRpIkSdPKQ3AlSZIkSU0YQCVJkiRJTTQ5BDfJCcDhwLVV9fBu3inAPt2QxcDqqtp3wLpXArcAtwHrqmppi5olSRpn9lZJ0iRqdQ7oicD7gY/OzqiqP5idTvJu4Kb1rP+kqrpui1UnSdLkORF7qyRpwjQJoFV1VpK9By1LEuB5wO+1qEWSpK2BvVWSNInG4RzQxwPXVNUl8ywv4KtJlic5pmFdkiRNKnurJGksjcNtWI4CTl7P8sdW1cok9wbOTHJxVZ01d1DXQI8B2HPPPbdMpZIkTYah9Fawv0qShmuke0CTLAKeDZwy35iqWtn9ey1wKnDAPOOOr6qlVbV0yRDudyhJ0iQaZm/txthfJUlDM+pDcJ8CXFxVKwYtTLJ9kh1np4FDgAsb1idJ0qSxt0qSxlaTAJrkZOC7wD5JViR5WbfoSOYcIpRktySnd0/vA3wnyfnA94EvVdVXWtQsSdI4s7dKkiZRq6vgHjXP/BcPmLcSOKybvhx45BYtTpKkCWRvlSRNolEfgitJkiRJmhIGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhNNAmiSE5Jcm+TCvnnvSPKTJOd1j8PmWffQJD9McmmSN7WoV5KkcWdvlSRNolZ7QE8EDh0w/71VtW/3OH3uwiTbAP8MPA14KHBUkodu0UolSZoMJ2JvlSRNmCYBtKrOAm5YwKoHAJdW1eVVtRb4JHDEUIuTJGkC2VslSZNo1OeAvibJD7rDiO45YPnuwFV9z1d08yRJ0mD2VknS2BplAP1X4DeBfYGrgXcPGJMB82rQxpIck2RZkmWrVq0aXpWSJE2OofZWsL9KkoZrZAG0qq6pqtuq6nbgA/QOCZprBbBH3/P7ASvn2d7xVbW0qpYuWbJk+AVLkjTmht1bu23aXyVJQzOyAJpk176nzwIuHDDsHOBBSe6fZDvgSOC0FvVJkjRp7K2SpHG3qMWLJDkZOAjYJckK4O3AQUn2pXfYz5XAK7uxuwEfrKrDqmpdktcAZwDbACdU1UUtapYkaZzZWyVJk6hJAK2qowbM/tA8Y1cCh/U9Px2402XkJUmaZvZWSdIkGvVVcCVJkiRJU8IAKkmSJElqwgAqSZIkSWrCACpJkiRJasIAKkmSJElqwgAqSZIkSWrCACpJkiRJasIAKkmSJElqwgAqSZIkSWrCACpJkiRJasIAKkmSJElqwgAqSZIkSWrCACpJkiRJasIAKkmSJElqwgAqSZIkSWrCACpJkiRJasIAKkmSJElqwgAqSZIkSWrCACpJkiRJasIAKkmSJElqwgAqSZIkSWrCACpJkiRJasIAKkmSJElqwgAqSZIkSWrCACpJkiRJasIAKkmSJElqwgAqSZIkSWrCACpJkiRJaqJJAE1yQpJrk1zYN+9dSS5O8oMkpyZZPM+6Vya5IMl5SZa1qFeSpHFnb5UkTaJWe0BPBA6dM+9M4OFV9dvAj4A3r2f9J1XVvlW1dAvVJ0nSpDkRe6skacI0CaBVdRZww5x5X62qdd3Ts4H7tahFkqStgb1VkjSJxuUc0JcCX55nWQFfTbI8yTENa5IkaZLZWyVJY2fRqAtI8hfAOuAT8wx5bFWtTHJv4MwkF3ff+s7dzjHAMQB77rnnFqtXkqRxN6ze2m3L/ipJGpqR7gFNcjRwOPCCqqpBY6pqZffvtcCpwAHzjDu+qpZW1dIlS5ZsqZIlSRprw+yt3Rj7qyRpaEYWQJMcCrwReEZV/WyeMdsn2XF2GjgEuHDQWEmSpp29VZI07lrdhuVk4LvAPklWJHkZ8H5gR3qH/pyX5Lhu7G5JTu9WvQ/wnSTnA98HvlRVX2lRsyRJ48zeKkmaRE3OAa2qowbM/tA8Y1cCh3XTlwOP3IKlSZI0keytkqRJNC5XwZUkSZIkbeUMoJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJjY5gCZ5+JYoRJKkaWVvlSRNi4XsAT0uyfeT/HGSxUOvSJKk6WNvlSRNhU0OoFX1OOAFwB7AsiQnJTl46JVJkjQl7K2SpGmxoHNAq+oS4K3AG4EnAv+Y5OIkzx5mcZIkTQt7qyRpGizkHNDfTvJeYAb4PeDpVfVb3fR7h1yfJElbPXurJGlaLFrAOu8HPgC8pap+PjuzqlYmeevQKpMkaXrYWyVJU2EhAfQw4OdVdRtAkrsAd6uqn1XVx4ZanSRJ08HeKkmaCgs5B/Q/gLv3Pb9HN0+SJC2MvVWSNBUWEkDvVlW3zj7ppu8xvJIkSZo69lZJ0lRYSABdk2S/2SdJ9gd+vp7xkiRp/eytkqSpsJBzQF8HfDrJyu75rsAfDK8kSZKmjr1VkjQVNjmAVtU5SR4C7AMEuLiqfjn0yiRJmhL2VknStFjIHlCARwF7d+v/ThKq6qNDq0qSpOljb5UkbfU2OYAm+Rjwm8B5wG3d7AJskpIkLYC9VZI0LRayB3Qp8NCqqmEXI0nSlLK3SpKmwkIC6IXAfYGrh1yLJEnTyt4qNXLB2WezdvXqzd7OFTMz7L9kyRAqkqbLQgLoLsB/J/k+8D+zM6vqGUOrSpKk6WJvlRpZu3r1UILjJcuWDaEaafosJIC+Y9hFSJI05d4x6gIkSWphIbdh+VaSvYAHVdV/JLkHsM3wS5MkaTrYWyVJ0+Ium7pCklcAnwH+rZu1O/D5YRYlSdI0sbdKkqbFJgdQ4NXAY4GbAarqEuDe61shyQlJrk1yYd+8nZOcmeSS7t97zrPu0d2YS5IcvYB6JUkad5vcW8H+KkmaPAsJoP9TVWtnnyRZRO9eZetzInDonHlvAr5WVQ8CvtY9v4MkOwNvBx4NHAC8fb5GKknSBFtIbwX7qyRpwiwkgH4ryVuAuyc5GPg08O/rW6GqzgJumDP7COAj3fRHgGcOWPWpwJlVdUNV3QicyZ0brSRJk26TeyvYXyVJk2chAfRNwCrgAuCVwOnAWxewnftU1dUA3b+DDjXaHbiq7/mKbp4kSVuTYfVWsL9KksbYQq6Cezvwge6xpWVQCQMHJscAxwDsueeeW7ImqRlvli1Nh8a9FeyvkqQR2eQAmuQKBjSpqnrAJm7qmiS7VtXVSXYFrh0wZgVwUN/z+wHfHLSxqjoeOB5g6dKlG3PejDT2vFm2NB2G2FvB/ipJGmObHECBpX3TdwOeC+y8gO2cBhwN/H337xcGjDkD+Lu+CyMcArx5Aa8lSdI4G1ZvBfurJGmMbfI5oFV1fd/jJ1X1D8DvrW+dJCcD3wX2SbIiycvoNcaDk1wCHNw9J8nSJB/sXusG4K+Bc7rHsd08SZK2GgvprWB/lSRNnoUcgrtf39O70PvWdsf1rVNVR82z6MkDxi4DXt73/ATghE2tU5KkSbGQ3gr2V0nS5FnIIbjv7pteB1wJPG8o1UiSNJ3srZKkqbCQq+A+aUsUIknStLK3ams2rCu6b7d4MY848MAhVCRplBZyCO7r17e8qt6z8HIkSZo+9lZtzYZ1Rfflq1YNoRpJo7bQq+A+it5V9gCeDpzFHW9oLUmSNp69VZI0FRYSQHcB9quqWwCSvAP4dFW9fL1rSZKk+dhbJUlTYZNvwwLsCazte74W2Hso1UiSNJ3srZKkqbCQPaAfA76f5FSggGcBHx1qVZIkTRd7qyRpKizkKrh/m+TLwOO7WS+pqv8ablmSJE0Pe6skaVos5BBcgHsAN1fV+4AVSe4/xJokSZpG9lZJ0lZvkwNokrcDbwTe3M3aFvj4MIuSJGma2FslSdNiIXtAnwU8A1gDUFUrgR2HWZQkSVPG3ipJmgoLCaBrq6roXSSBJNsPtyRJkqaOvVWSNBUWEkA/leTfgMVJXgH8B/CB4ZYlSdJUsbdKkqbCQq6C+/8mORi4GdgHeFtVnTn0yiRJmhL2VknStNikAJpkG+CMqnoKYGOUJGkz2VslSdNkkw7BrarbgJ8l2WkL1SNJ0lSxt0qSpskmH4IL/AK4IMmZdFfrA6iq1w6tKkmSpou9VZI0FRYSQL/UPSRJ0nDYWyVJU2GjA2iSPavq/1bVR7ZkQZIkTQt7qyRp2mzKOaCfn51I8tktUIskSdPG3ipJmiqbEkDTN/2AYRciSdIUsrdKkqbKpgTQmmdakiQtjL1VkjRVNuUiRI9McjO9b2vv3k3TPa+q+o2hVydJ0tbN3ipJmiobHUCrapstWYgkSdPG3ipJmjYLuQ2LpCl12czMZm9ju8WLecSBBw6hGkmSJE0aA6ikjXb7mjXsv2TJZm1j+apVQ6pGkiRJk2ZTLkIkSZIkSdKCGUAlSZIkSU2MNIAm2SfJeX2Pm5O8bs6Yg5Lc1DfmbaOqV5KkcWdvlSSNs5GeA1pVPwT2BUiyDfAT4NQBQ79dVYe3rE2SpElkb5UkjbNxOgT3ycBlVfXjURciSdJWwt4qSRor4xRAjwROnmfZY5Kcn+TLSR7WsihJkiaYvVWSNFbGIoAm2Q54BvDpAYvPBfaqqkcC/wR8fp5tHJNkWZJlq7zNgyRpyg2jt3bbsb9KkoZmLAIo8DTg3Kq6Zu6Cqrq5qm7tpk8Htk2yy4Bxx1fV0qpaumQz71MoSdJWYLN7a7fc/ipJGppxCaBHMc8hQknumyTd9AH0ar6+YW2SJE0ie6skaeyM9Cq4AEnuARwMvLJv3qsAquo44DnAHyVZB/wcOLKqahS1SpI0CeytkqRxNfIAWlU/A+41Z95xfdPvB97fui5JkiaVvVWSNK7G5RBcSZIkSdJWzgAqSZIkSWpi5IfgSuPkgrPPZu3q1Zu9ne0WL+YRBx44hIo0H39XkiRJk8cAKvVZu3o1+w/hNgPLvVfeFufvSpIkafJ4CK4kSZIkqQkDqCRJkiSpCQ/BlSRJ0ti7bGZmKNu5YmZmKKdwSFoYA6gkSZLG3u1r1gwlOF6ybNkQqpG0UB6CK0mSJElqwgAqSZIkSWrCACpJkiRJasIAKkmSJElqwgAqSZIkSWrCACpJkiRJasIAKkmSJElqwgAqSZIkSWrCACpJkiRJamLRqAuQtkaXzcwMZTtXzMyw/5IlQ9mWJEmSNGoGUGkLuH3NmqEEx0uWLRtCNZIkSdJ48BBcSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhOLRl2AJEmSNK0um5kZyna2W7yYRxx44FC2JW1JIw+gSa4EbgFuA9ZV1dI5ywO8DzgM+Bnw4qo6t3WdkiRNEvurNBluX7OG/Zcs2eztLF+1agjVwAVnn83a1as3axuGYa3PyANo50lVdd08y54GPKh7PBr41+5fSZK0fvZXSZtk7erVmx2IhxWGtXWahHNAjwA+Wj1nA4uT7DrqoiRJmnD2V0lSc+MQQAv4apLlSY4ZsHx34Kq+5yu6eZIkaX72V0nS2BmHQ3AfW1Urk9wbODPJxVV1Vt/yDFin5s7omusxAHvuueeWqVSSpMlhf5UkjZ2R7wGtqpXdv9cCpwIHzBmyAtij7/n9gJUDtnN8VS2tqqVLhnAityRJk8z+KkkaRyMNoEm2T7Lj7DRwCHDhnGGnAS9Kz4HATVV1deNSJUmaGPZXSdK4GvUhuPcBTu1dCZ5FwElV9ZUkrwKoquOA0+ldIv5SepeJf8mIapUkaVLYXyVJY2mkAbSqLgceOWD+cX3TBby6ZV2SJE0y+6skaVyN/BxQSZIkSdJ0MIBKkiRJkpowgEqSJEmSmjCASpIkSZKaMIBKkiRJkpowgEqSJEmSmjCASpIkSZKaMIBKkiRJkpowgEqSJEmSmjCASpIkSZKaWDTqAiRNl8tmZoaynStmZth/yZKhbEuSJEltGEAlNXX7mjVDCY6XLFs2hGokSZLUkofgSpIkSZKaMIBKkiRJkpowgEqSJEmSmjCASpIkSZKaMIBKkiRJkpowgEqSJEmSmjCASpIkSZKaMIBKkiRJkpowgEqSJEmSmjCASpIkSZKaMIBKkiRJkpowgEqSJEmSmjCASpIkSZKaMIBKkiRJkpowgEqSJEmSmjCASpIkSZKaWDTKF0+yB/BR4L7A7cDxVfW+OWMOAr4AXNHN+lxVHduyTkmSJoW9VZpOl83MDGU7V8zMsP+SJUPZljTISAMosA54Q1Wdm2RHYHmSM6vqv+eM+3ZVHT6C+iRJmjT2VmkK3b5mzVCC4yXLlg2hGml+Iz0Et6qurqpzu+lbgBlg91HWJEnSJLO3SpLG2dicA5pkb+B3gO8NWPyYJOcn+XKShzUtTJKkCWVvlSSNm1EfggtAkh2AzwKvq6qb5yw+F9irqm5NchjweeBBA7ZxDHAMwJ577rmFK5YkabwNo7d227G/SpKGZuR7QJNsS69BfqKqPjd3eVXdXFW3dtOnA9sm2WXAuOOramlVLV3iidOSpCk2rN7aLbe/SpKGZqQBNEmADwEzVfWeecbctxtHkgPo1Xx9uyolSZoc9lZJ0jgb9SG4jwVeCFyQ5Lxu3luAPQGq6jjgOcAfJVkH/Bw4sqpqFMVKkjQB7K2SpLE10gBaVd8BsoEx7wfe36YiSZImm71VkjTORr0HVJIkSWPqgrPPZu3q1Zu1jStmZoZyf0pJWwcDqCRJkgZau3r1ZofHS5YtG1I1krYGBlBJU+2ymZmhbGe7xYt5xIEHDmVbkiRJWysDqKSpdvuaNUM5NGz5qlVDqEaSJGnrNvL7gEqSJEmSpoMBVJIkSZLUhAFUkiRJktSEAVSSJEmS1IQBVJIkSZLUhAFUkiRJktSEAVSSJEmS1IQBVJIkSZLUxKJRFyBJkiRp63HZzMxQtrPd4sU84sADh7ItjQ8DqCRJkqShuX3NGvZfsmSzt7N81aohVKNx4yG4kiRJkqQmDKCSJEmSpCYMoJIkSZKkJjwHVFuFC84+m7WrV2/2dq6YmRnKOQuSJEmS7swAqq3C2tWrhxIcL1m2bAjVSJIkSRrEQ3AlSZIkSU0YQCVJkiRJTRhAJUmSJElNGEAlSZIkSU0YQCVJkiRJTRhAJUmSJElNGEAlSZIkSU0YQCVJkiRJTRhAJUmSJElNjDyAJjk0yQ+TXJrkTQOW3zXJKd3y7yXZu32VkiRNDnurJGlcjTSAJtkG+GfgacBDgaOSPHTOsJcBN1bVA4H3Au9sW6UkSZPD3ipJGmej3gN6AHBpVV1eVWuBTwJHzBlzBPCRbvozwJOTpGGNkiRNEnurJGlsjTqA7g5c1fd8RTdv4JiqWgfcBNyrSXWSJE0ee6skaWylqkb34slzgadW1cu75y8EDqiqP+kbc1E3ZkX3/LJuzPVztnUMcEz3dB/gh0MocRfguiFsp6VJq3nS6gVrbmHS6gVrbmFY9e5VVUuGsJ2xNMze2i0bdn+dtM8dWHMLk1YvWHMLk1YvTF7NzXvroiG82OZYAezR9/x+wMp5xqxIsgjYCbhh7oaq6njg+GEWl2RZVS0d5ja3tEmredLqBWtuYdLqBWtuYdLqHaGh9VYYfn+dxN+jNW95k1YvWHMLk1YvTF7No6h31IfgngM8KMn9k2wHHAmcNmfMacDR3fRzgK/XKHfbSpI03uytkqSxNdI9oFW1LslrgDOAbYATquqiJMcCy6rqNOBDwMeSXErv29kjR1exJEnjzd4qSRpnoz4El6o6HTh9zry39U3/Anhu67o6Qz2kt5FJq3nS6gVrbmHS6gVrbmHS6h0Ze+vQWfOWN2n1gjW3MGn1wuTV3LzekV6ESJIkSZI0PUZ9DqgkSZIkaUoYQIEkhyb5YZJLk7xpwPK7JjmlW/69JHu3r/IO9eyR5BtJZpJclORPB4w5KMlNSc7rHm8btK1WklyZ5IKulmUDlifJP3bv8Q+S7DeKOvvq2afvvTsvyc1JXjdnzMjf4yQnJLk2yYV983ZOcmaSS7p/7znPukd3Yy5JcvSgMY3qfVeSi7vf+6lJFs+z7no/Q41rfkeSn/T97g+bZ931/m1pXPMpffVemeS8edZt/j7P9zdtnD/L2jB7axuT1F/trc1rHtv+am9tUu/49taqmuoHvQs0XAY8ANgOOB946Jwxfwwc100fCZwy4pp3BfbrpncEfjSg5oOAL476/e2r50pgl/UsPwz4MhDgQOB7o655zmfkp/TubzRW7zHwBGA/4MK+ef8HeFM3/SbgnQPW2xm4vPv3nt30PUdU7yHAom76nYPq3ZjPUOOa3wH82UZ8btb7t6VlzXOWvxt427i8z/P9TRvnz7KPDf5O7a3t6p7I/mpvbVLz2PZXe2uTese2t7oHFA4ALq2qy6tqLfBJ4Ig5Y44APtJNfwZ4cpI0rPEOqurqqjq3m74FmAF2H1U9Q3IE8NHqORtYnGTXURfVeTJwWVX9eNSFzFVVZ3Hne/f1f14/AjxzwKpPBc6sqhuq6kbgTODQLVZoZ1C9VfXVqlrXPT2b3j0Lx8Y87/HG2Ji/LVvE+mru/nY9Dzi5RS0bYz1/08b2s6wNsreOj3Htr/bWIZq0/mpv3fLGubcaQHu/iKv6nq/gzg3nV2O6/5BvAu7VpLoN6A5Z+h3gewMWPybJ+Um+nORhTQu7swK+mmR5kmMGLN+Y38OoHMn8f1DG6T2edZ+quhp6f3yAew8YM67v90vpfVM/yIY+Q629pjus6YR5Dl8Z1/f48cA1VXXJPMtH+j7P+Zs2yZ/laWdvbWdS+6u9ta1J6a/21i1g3HqrAbR3SMpccy8NvDFjmkuyA/BZ4HVVdfOcxefSO6zlkcA/AZ9vXd8cj62q/YCnAa9O8oQ5y8f1Pd4OeAbw6QGLx+093hRj934n+QtgHfCJeYZs6DPU0r8CvwnsC1xN77CbucbuPe4cxfq/oR3Z+7yBv2nzrjZg3ji8z9PO3trOxPVXe2tbE9Rf7a1bwDj2VgNoL9Hv0ff8fsDK+cYkWQTsxMIOGxiaJNvS+zB9oqo+N3d5Vd1cVbd206cD2ybZpXGZ/fWs7P69FjiV3iEU/Tbm9zAKTwPOrapr5i4Yt/e4zzWzh1d1/147YMxYvd/dye2HAy+o7uSDuTbiM9RMVV1TVbdV1e3AB+apZazeY/jV369nA6fMN2ZU7/M8f9Mm7rOsX7G3NjKh/dXe2sgk9Vd76xapbSx7qwEUzgEelOT+3TdyRwKnzRlzGjB79afnAF+f7z/iFrrjzD8EzFTVe+YZc9/Zc2mSHEDvd319uyrvUMv2SXacnaZ3UvyFc4adBrwoPQcCN80eHjBi836jNU7v8Rz9n9ejgS8MGHMGcEiSe3aHuBzSzWsuyaHAG4FnVNXP5hmzMZ+hZuacP/WseWrZmL8trT0FuLiqVgxaOKr3eT1/0ybqs6w7sLc2MMH91d7awKT1V3vrcI11b62GV2Ma1we9K8T9iN5Vtf6im3csvf9gAe5G7zCRS4HvAw8Ycb2Po7cb/AfAed3jMOBVwKu6Ma8BLqJ3dbCzgd8dYb0P6Oo4v6tp9j3urzfAP3e/gwuApWPwubgHvaa3U9+8sXqP6TXwq4Ff0vu26mX0zqH6GnBJ9+/O3dilwAf71n1p95m+FHjJCOu9lN55BrOf5dmrYu4GnL6+z9AIa/5Y9zn9Ab0/5LvOrbl7fqe/LaOquZt/4uznt2/syN/n9fxNG9vPso+N+r3aW7d8zRPXX7G3tqx5bPvrPPXaW4db79j21nQvIEmSJEnSFuUhuJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJgygkiRJkqQmDKDSmEpyW5LzkuCXC4oAACAASURBVFyY5NNJ7rEFX+vYJE/ppl+3JV9LkqRRsbdKo+dtWKQxleTWqtqhm/4EsLzmuTn6nPVC77/t2xf4ulfSu0/cdQtZX5KkcWVvlUbPPaDSZPg28ECAJK/vvrm9MMnrunl7J5lJ8i/AucAeSY5KckE37p3duG2SnNjNuyDJ/+rmn5jkOUleS+/myd9I8o0kL0vy3tkikrwiyQYbtSRJE8DeKo3AolEXIGn9kiwCngZ8Jcn+wEuARwMBvpfkW8CNwD7AS6rqj5PsBrwT2L9b9tUkzwSuAnavqod3217c/1pV9Y9JXg88qaquS7I98IMkf15Vv+xe+5UNfmxJkrYYe6s0Ou4BlcbX3ZOcBywD/i/wIeBxwKlVtaaqbgU+Bzy+G//jqjq7m34U8M2qWlVV64BPAE8ALgcekOSfkhwK3Ly+AqpqDfB14PAkDwG2raoLhvtjSpLUjL1VGjH3gErj6+dVtW//jO4clPms6R86aEBV3ZjkkcBTgVcDzwNeuoE6Pgi8BbgY+PCGipYkaYzZW6URcw+oNFnOAp6Z5B7dITzPoncOy1zfA56YZJck2wBHAd9Ksgtwl6r6LPCXwH4D1r0F2HH2SVV9D9gDeD5w8lB/GkmSRs/eKjXkHlBpglTVuUlOBL7fzfpgVf1Xkr3njLs6yZuBb9D7xvb0qvpC9w3th5PMfvn05gEvczzw5SRXV9WTunmfAvatqhuH+xNJkjRa9lapLW/DImmDknwReG9VfW3UtUiStDWwt2paeQiupHklWZzkR/TOmbFBSpK0meytmnbuAZUkSZIkNeEeUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUEmSJElSEwZQSZIkSVITBlBJkiRJUhMGUKmT5KIkB426Dm2aJN9M8vJu+sVJvrPA7RyX5C+HW50kSZL6GUA1FpJcmeTnSW5Nck2SDyfZoWUNVfWwqvpmV887knx8c7eZ5MQk65LsttkFbsWSPDjJp5Ncl+SmJD9I8vok2wz5dV6W5OIkt3Sfsy8l2RGgql5VVX89zNeTJEnSHRlANU6eXlU7APsBjwLeuqkbSLJo6FUtUJLtgd8HbgJesIVeY2x+3oVK8pvA94CrgEdU1U7Ac4GlwI5DfJ0nAn8HHFVVOwK/BXxqWNuXJEnShhlANXaq6ifAl4GHAyTZLclpSW5IcmmSV8yO7fZUfibJx5PcDLw4yV2T/EOSld3jH5LctRu/S5IvJlndbe/bSe7SLbsyyVOSHAq8BfiDbo/s+Umem2R5f51J3pDk8+v5UX4fWA0cCxzdt95u3d7enfvm/U6392/b7vlLk8wkuTHJGUn26htbSV6d5BLgkm7e+5JcleTmJMuTPL5v/N2TfKTb1kySP0+yYk49n02yKskVSV476IdJcmCSn/bvlUzyrCQ/6KYPSLKsq+GaJO9Zz3vT76+A/6yq11fV1QBV9cOqen5Vre577f/sfm/nL/BQ6UcB362q/+pe44aq+khV3dK9xolJ/qab/vfudz/7uD3Ji7tlD0lyZvf5+WGS5y2gFkmSpKlkANXYSbIHcBjwX92sk4EVwG7Ac4C/S/LkvlWOAD4DLAY+AfwFcCCwL/BI4AB+vTf1Dd22lgD3oRc0q//1q+or9PaUnVJVO1TVI4HTgPsn+a2+oX8IfGw9P8rRXe2fBB6SZL9u+yuB79ILqLOeD3ymqn6Z5JldXc/u6vx2t51+zwQeDTy0e35O9/PuDJwEfDrJ3bplbwf2Bh4AHNzVDUAXvv8dOB/YHXgy8LokT537w1TV2cAa4Pfm1H1SN/0+4H1V9RvAb7LxexefQu/3N1CS3YEvAX/T/Xx/Bnw2yZKN3P6s7wFPTfJXSR47+6XEIFX19O53vwO9z9xPga91e7XPpPcz3xs4CviXJA/bxFokSZKmkgFU4+TzSVYD3wG+RS9o7gE8DnhjVf2iqs4DPgi8sG+971bV56vq9qr6Ob3DXY+tqmurahW9PWyz438J7ArsVVW/rKpvV9UdAuggVfU/wCl04a0LHHsDXxw0PsmewJOAk6rqGuBr9O0FpRdgjurGBjiSXwe5VwL/u6pmqmodvTC8b/9e0G75Dd3PS1V9vKqur6p1VfVu4K7APt3Y5wF/V1U3VtUK4B/7tvMoYElVHVtVa6vqcuADXT2DnNxX9470viiYDce/BB6YZJequrULrBvjXsDV61n+h8DpVXV69zs+E1jWvfZGq6pv0wv1+9ELtNcneU/Wc55pkgcDHwX+oKquAg4HrqyqD3fv9bnAZ+mFVEmSJG2AAVTj5JlVtbiq9qqqP+7C1W7ADbOHSXZ+TG9v3ayr5mxnt25M//jZiwC9C7gU+GqSy5O8aRPq+wjw/C4wvhD4VBdMB3khMNMFZujtmX3+7CG29Pb4PSa9ixM9gd5e2G93y/YC3tcdbroauAEI6/mZu8OBZ9K7gM9qYCdgl27xbnPG90/vBew2+1rdum+ht3d4kJOAZ3d7D58NnFtVs+/1y4AHAxcnOSfJ4fNsY67r6X0pMJ+9gOfOqfFxG1hnoKr6clU9nd6e1COAFwMvHzQ2yU7AF4C/7MLrbC2PnlPLC4D7bmotkiRJ02jiL2Cird5KYOckO/aF0D2Bn/SNmbsHcyW9oHBR3/iVAN023gC8oduL+Y0k51TV1+Zs4057Ravq7CRrgcfTO/T0+eup+0XAnkl+2j1fRG9P39OA06pqdZKv0ts7+VvAyX17Yq8C/raqPrGe7f+qvu58zzfSO3z2oqq6PcmN9EIr9PYu3g/47+75Hn3buQq4oqoetJ7X+vWLVv13kh93P0f/4bdU1SXAUd1hvc8GPpPkXlW1ZgOb/Q96hyN/eJ7lVwEfq6pXzLN8k1XV7fQOqf063bnG/bqf4STgG1X1b3Nq+VZVHTysWiRJkqaJe0A11rrDHv8T+N9J7pbkt+ntaVtfODsZeGuSJUl2Ad4GfBwgyeFJHtjtxbwZuK17zHUNsHcXRPp9FHg/sK6qBt5vMslj6J0DeQC98zL3pRdyTuLOh+G+iF74Oqlv/nHAm2fPK0yyU5Lnrufn3RFYB6wCFiV5G/Abfcs/1W3vnt35lK/pW/Z94OYkb+wuVrRNkocnedR6Xu8k4LX09tx+uu/n/sMkS7pwt7qbPei9nevtwO8meVeS+3bbemB6F5ZaTO939/QkT+3qu1uSg5LcbyO2/StJjkhyZPc+JMkBwBOBQYcK/y2wPfCnc+Z/EXhwkhcm2bZ7PGrOucGSJEmahwFUk+AoeudbrgROBd7enQc4n7+hd47gD4ALgHO7eQAPorfH7VZ6FwL6l9l7f84xG6yuT3Ju3/yP0QuTG7r40Beq6oKq+unsg95Feg7Pr69+e1pXzzVVdf7sylV1KvBO4JPpXdn3Qnp7HOdzBr2rBv+I3uHGv+COh9keS+/CS1d0P/tngP/pXus24On0QvIVwHX0zrHdaT2vdzJwEPD1qrqub/6hwEVJbu1+1iOr6hcA3ZVkH3+nLfVquAx4DL3f8UVJbqJ3XuUy4JbuS4gj6B0avKr72f4fNv3v143AK+hdOfhmesH2XfPsaT6K3oWsbsyvr4T7gm4P+iH0zpFdSe/iRO+kd86tJEmSNiAbcf0VSZ0kdweuBfbrDjmdOEn+iF44fOKoa5EkSdJ0cQ+otGn+CDhnksJnkl27247cJck+9M6BPXXUdUmSJGn6NAmgSU5Icm2SC/vmvSvJxUl+kOTU7lyvQesemt7N3i/dxCuWSkOV5Ep65wS+YcSlbKrtgH8DbgG+Tu/Krv8y0ookSZI0lZocgpvkCfTOuftoVT28m3cIvXPI1iV5J0BVvXHOetvQO6/tYHrnsJ0DHFVV/40kSZIkaaI02QNaVWfRu5dh/7yvVtW67unZ9G4TMdcBwKVVdXlVrQU+Se9iJJIkSZKkCTMu9wF9KXDKgPm7c8erea4AHj1oA0mOAY4B2H777fd/yEMeMuwaJUlbgeXLl19XVUtGXYckSdNo5AE0yV/Qu4fhoFshZMC8gccMV9XxwPEAS5curWXLlg2tRknS1iPJj0ddgyRJ02qkATTJ0cDhwJNr8MmoK4A9+p7fj9699yRJkiRJE2Zkt2FJcijwRuAZVfWzeYadAzwoyf2TbEfv5u+ntapRkiRJkjQ8rW7DcjLwXWCfJCuSvAx4P7AjcGaS85Ic143dLcnpAN1Fil4DnAHMAJ+qqota1CxJkiRJGq4mt2FpzXNAJUnzSbK8qpaOug5JkqbRyA7BlSRJkiRNFwOoJEmSJKkJA6gkSZIkqQkDqCRJkiSpCQOoJEmSJKkJA6gkSZIkqQkDqCRJkiSpCQOoJEmSJKkJA6gkSZIkqQkDqCRJkiSpCQOoJEmSJKkJA6gkSZIkqQkDqCRJkiSpCQOoJEmSJKkJA6gkSZIkqQkDqCRJkiSpCQOoJEmSJKkJA6gkSZIkqQkDqCRJkiSpCQOoJEmSJKkJA6gkSZIkqQkDqCRJkiSpCQOoJEmSJKkJA6gkSZIkqQkDqCRJkiSpCQOoJEmSJKkJA6gkSZIkqQkDqCRJkiSpiSYBNMkJSa5NcmHfvOcmuSjJ7UmWrmfdK5NckOS8JMta1CtJkiRJGr5We0BPBA6dM+9C4NnAWRux/pOqat+qmjeoSpIkSZLG26IWL1JVZyXZe868GYAkLUqQJEmSJI3YJJwDWsBXkyxPcsx8g5Ick2RZkmWrVq1qWJ4kSZIkaWNMQgB9bFXtBzwNeHWSJwwaVFXHV9XSqlq6ZMmSthVKkiRJkjZo7ANoVa3s/r0WOBU4YLQVSZIkSZIWYqwDaJLtk+w4Ow0cQu/iRZIkSZKkCdPqNiwnA98F9kmyIsnLkjwryQrgMcCXkpzRjd0tyendqvcBvpPkfOD7wJeq6istapYkSZIkDVerq+AeNc+iUweMXQkc1k1fDjxyC5YmSZIkSWpkrA/BlSRJkiRtPQygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJgygkiRJkqQmDKCSJEmSpCYMoJIkSZKkJpoE0CQnJLk2yYV9856b5KIktyf5/9u79/jKzvKw979H9/tlJM3VM57xeGwwYHyZEBwKIU0gwCEh15a0aQihoaSkSZr2NPRyQkrPaUia5jRt2hISaKBtIORaaEiAJgGSHiAeg23M1caMzczIMxrdt+5bes4f2nshyRpbmpG2ZPn3/Xz2Z+937bXWftbeGs1+9L7v855+gmNfFhFfioiHIuJNtYhXkiRJkrT1atUD+pvAy9ZsewD4HuDjVzooIuqB/wi8HLgF+IGIuGWbYpQkSZIkbaOaJKCZ+XFgZM22L2Tml57k0OcBD2Xmw5k5D7wXeNU2hSlJkiRJ2ka7fQ7oEeBrK9rnKtseJyJeHxFnIuLM0NBQTYKTJEmSJG3cbk9AY51tud6Omfn2zDydmacHBga2OSxJkiRJ0mbt9gT0HHB0Rfs64MIOxSJJkiRJuga7PQG9GzgVESciogl4NfD+HY5JkiRJknQVarUMy3uATwA3R8S5iHhdRHx3RJwD7gL+KCI+VNn3cER8ECAzy8CPAx8CvgC8LzM/V4uYJUmSJElbKzLXnVL5lHb69Ok8c+bMTochSdqFIuKezLzi+tOSJGn77PYhuJIkSZKkPcIEVJIkSZJUEyagkiRJkqSaMAGVJEmSJNWECagkSZIkqSZMQCVJkiRJNWECKkmSJEmqCRNQSZIkSVJNmIBKkiRJkmrCBFSSJEmSVBMmoJIkSZKkmjABlSRJkiTVhAmoJEmSJKkmTEAlSZIkSTVhAipJkiRJqgkTUEmSJElSTZiASpIkSZJqomGzB0REL3AYmAHOZubSlkclSZIkSdpzNpSARkQ38EbgB4AmYAhoAQ5ExCeB/5SZf75tUUqSJEmSnvI22gP6u8C7gRdm5tjKJyLiTuDvRMQNmfmOrQ5QkiRJkrQ3bCgBzcyXPMFz9wD3bFlEkiRJkqQ9adNzQAEiYgD4SaAV+M+Z+dCWRiVJkiRJ2nOutgruvwU+DvwJ8J6tC0eSJEmStFdtKAGNiD+JiBeu2NQEnK3cmrc+LEmSJEnSXrPRHtC/CbwqIn4rIk4C/xfws8Bbgb+/XcFJkiRJkvaOjRYhGgf+cUTcAPw/wHngjZXtkiRJkiQ9qY2uA3oD8GPAAvCPgJPA+yLif7K8Buji9oUoSZIkSdoLNjoE9z0sFxz6JPBfM/MvMvPbgQngw9sVnCRJkiRp79hoAtoCfLVya6tuzMx3Aa98soMj4p0RcSkiHlixbV9EfCQiHqzc917h2MWIuLdye/8G45UkSZIk7TIbXQf07wP/BpgH3rDyicyc2cDxvwn8KvDuFdveBPxpZr41It5Uaf/MOsfOZOZtG4wTgPHxcT71qU9x0003MT4+zszMDO3t7fT09DA8PMzs7CydnZ10dnYyPDzM/Pw8nZ2dtLe3c/nyZcrlMt3d3fT29jI6Osri4iK9vb20t7dvJgxJkiRJ0gqRmbV5oYjjwP/MzGdX2l8CXpyZgxFxCPhoZt68znGlzOzYzGvdcMMN+eY3v5mxsTHuuusu6uvrmZ+f5/z581x//fXU1dUxOzvLY489xvHjxwGYnp7m8uXLHDt2DIDZ2VlGR0c5dOhQcd5jx44xMDBwVdcvSdodIuKezDy903FIkvR0tNF1QD8QEa+MiMZ1nrshIt4SET+yydc+kJmDAJX7/VfYryUizkTEJyPiuzZ68lKpxMTEBBcvXgRgbGyM2dlZSqVS0Z6ZmWFqagqAkZERpqammJ2dBeDy5cuMjY2xsLBQnPP8+fMsLS1t8jIlSZIkSbDxIbg/Cvw08CsRMQIMsTwv9DjwFeBXM/N/bEuEcCwzL1Qq8f5ZRHw2M7+ydqeIeD3weoC+vj4WF5cL887PzwMU7ep9uVxed3v1fm5urmg3NjYWjxcWFmhubt76q5QkSZKkPW6j64A+BvwT4J9UhtIeAmaAL2fm9FW+9sWIOLRiCO6lK7z2hcr9wxHxUeB2lpPetfu9HXg7LA/BbWtrY3JykgMHDgDQ2dnJ+Pg4HR0dRbtUKhXzOjs7O5mbm6O1tRWAtrY2MpOWlpbiNRobG2lqarrKy5UkSZKkp7eN9oAWMvMscHYLXvv9wGuAt1buH9eDWqmMO52ZcxHRD7wA+MWNnLyrq4tbb72VpqYmFhcX6enpYWBggLm5OZaWlujv7+fgwYPMzs6SmRw4cIDDhw8zNzdHZnLkyJGilxSgrq6OY8eOERFbcOmSnk4yk1KpRF1dHe3t7UW7vr6etrY2lpaWKJVKNDY20trayuLiIlNTUzQ1NdHS0kK5XGZ6eprm5maam5tZWFhgZmaGlpYWmpqainZrayuNjY3Mzc0xNzdHW1sbDQ0NzM7OMj8/T3t7O/X19czMzLCwsEBHRwd1dXVMT0+zuLhIR0cHEcHU1BRLS0tFuzp1ofoHPEmSpKu16QT0akTEe4AXA/0RcQ54M8uJ5/si4nXAo8D3V/Y9DbwhM/8u8Ezg1yJiieX5qm/NzM8/2et1dXXxvd/7vdTX17O0tMTc3BzNzc3U1dWxuLjI/Pw8LS0tRATlcplyuUxzc/OqdktLC5nJ2NgYi4uLdHd3F0NxJWmjZmZmeOihh4rpAFcjIlhZMK7ajgiWlpaoq6tb1a7+oWxtGyj2u5KVz6993ebmZk6dOuU0BEmSdNVqVgW3lk6fPp1nzpzZ0nNmJoODgwwNDbG0tMS+ffs4evQodXUbXUpV0tPRF7/4xaLYGcBDDz1EW1sbhw8fJjN58MEH6e7u5sCBAywuLvLggw/S399Pf38/5XKZhx56iIMHD9LT08Pc3BwPP/wwR44coauri+npaR555BGOHTtGe3s7k5OTnDt3jhMnTtDS0sLY2BiPPfYYJ0+epLGxkeHhYYaGhjh16hT19fVcunSJsbExbrzxRurq6hgcHGRqaoobb7wRgHPnzrGwsMCJEyeK+Lu6ujh16lTN38etZBVcSZJ2zqZ7QCOileXCQF/ahnh2rccee4zBwcGiffnyZTKzWMZFktaqDqWtmp+fZ2Fhodg2Ozu7ap+ZmRkyk6mpKfr7+5mamiqG6/b09BT7TU1N0dXVtard3t6+qt3S0kKpVCrOVz0+M5mZmaGjo4OpqSkWFxeL+e9TU1MsLCwUo0aqx5fLZRoaGshMHn74Yebm5ogI+vr6KJfLjI6OUl9fT19fH/Pz84yNjdHQ0EBfXx8zMzNMTEzQ2NhIX18fpVKJyclJmpub6evrY2JiglKpRGtrK729vYyNjTE9PU1bWxs9PT2Mjo4W8XZ1dTE8PMzc3BydnZ10dHQwPDzMwsICXV1dtLa2Mjw8TLlcpqenh6amJoaHh4u1nBsaGhgeHq7lj4AkSVpjUwloRHwH8EtAE3AiIm4D3pKZ37kdwe0m631pGRkZ4dixY/aCSk8B5XK5mDfZ2NjIwsICs7OztLa20tDQwPz8fDFvsr6+nrm5uWLeZHXt4HK5THt7OxHBzMwMi4uLRXt6eprMLAqbVZO9hoaGYj55fX09EVEM519739DQsO72jd4/2fHV51fuNzs7u2p7NdmsPl8ul6mvrwfg4sWLlEol+vv7Abj77ruLRBLgE5/4BJ2dnfT09DA/P8/nP/95+vr6iiJv999/P4cOHaKtrY25uTnuvfderrvuOlpaWpiZmeGee+7h+PHjNDY2cvnyZc6cOcOJEydoaGhgcHCQu+++mxtuuIG6ujrOnTvH4OAgN9xwAxHBV7/6VYaHh4s/Cn75y19mcnKSo0ePAvD5z3+eubk5Dh8+fBU/PZIkaatstgf054DnAR8FyMx7K1VxJWnXunjxIufPn3/c/MfMLOZPVqcjVJ/faLv6B6jqGsFr29ViQo2NjdTX17Nv3z7a2tqA5YSvt7eXrq4uAFpaWujq6mLfvn3AcjXuzs5Oent7geUiQB0dHfT09ADLw2EnJiaK43t6epienqazsxOAffv2sbCwUCTF+/btW1Xdu6+vj/r6+iJB7e/vZ3p6ukg4+/v7WVhYKOaCjo2NcfDgQWC5d3diYqLo6VxYWKBUKhVF32ZnZ5mZmWF0dJTOzk6mpqaYn59ndHSUapXyhYWF4pyTk5OUy2XGx8fp7+9nfHyccrnM5ORk0TNafY2uri5GR0eZn59namqKjo6Ooqd0dna2GH5cLbbU2NjI6Ogo5XK5qIwuSZJ2xmYT0HJmjj8dK8H29fVx4cKFVdv27dtn76d2tdnZWcbHx2lsbCzmEE5MTNDU1FQkK6VSiebmZrq7u5mamiqGb3Z3dzMxMcHMzEyRCI2PjzM7O0t7ezvt7e2Mj48XwyFbW1sZHR0thkM2NzcXX/p7enp2rHDNzMwM586dK9qlUolHH32U48eP09raytjYGOfPn+fkyZM0NTUxMjLCxYsXufHGG2loaGBoaIjh4WFOnTpFXV0djz32GBMTE9x4441EBOfOnWNmZoaTJ08C8Mgjj7C4uFj0xFV7ULu7u6mrq+Pmm28uhqnW19fzzGc+k+np6eJzuuWWWyiVSsXndMsttxTDVJubm3nWs57F+Pg4U1NTtLa28pznPIeRkZHic3ruc5/L8PAws7OzdHR08NznPpfLly8zPz9PZ2cnd9xxB0NDQywsLHD99dfT2trK5cuXWVxc5MSJEzQ3Nxdz3U+ePEl9fX0x5eDo0aNFMltN2quJeDXhXntfff7J7q90/NrzPNl5n+j4lfFKkqSdsdkE9IGI+FtAfUScAn4C+P+2Pqzd5+DBg2Tm44oQVYfWVYesSbvF5cuXeeSRR4r25ORkMdwUYGJioliWA2BsbIyurq7ijypjY2N0d3cXvV/j4+NFz9vS0hKTk5N0d3cDFMuEVHviqsNbqz1x586d4/jx48VQzVoaHx9f1V45T7I677HabmpqKobOVq+nVCqxtLTE9PQ0HR0dlEolyuUyc3NztLS0MDU1RblcLnraqsdXq9PCcvJzww03rIqj2ssJyz2d1WGtAK2trQwMDKxqr+y5q65XXLV2WOmRI0dWtavDUKuOHTu2ql1NKq/Urn6OAKOjo8Dy77y2traiN7W5uZmWlpZi3+qSMNWfierPWrVdXeKl2u7s7GRoaKhod3V1MTIyUpyvq6tr1VrO1SJM1Vi7urpYWFgo3puurq5irmn1/HNzc/6uliRph232f+J/APxzYA54D/Ah4F9tdVC7UURw+PDh4ove4uIiDz/8cPHltru7mxMnThRf5qWdtLS0tKrXr1wuc/78eXp6ejh48CBzc3OcP3+egYEB+vv7mZmZYXBwkMykt7eXUqnE4OAgdXV1xRf/wcFBGhoa6OjoYGxsjIsXLxZJx+joKJcvXy6SjuHh4aK6avUL/9e+9rUioa3+O7nSsNVqe3FxkYi4ppEGTU1Nq9pr50E+2X1jYyMzMzOr5lMuLCys2m9xcbG4poaGhsctfbI2hqeq66+/vhiKGxE85znPKdYwraur47bbbmNxcbFY4/TOO++kXC4zNTVFY2Mj3/iN31gMzW1paeGbvumbmJ6eLnrV77rrrmKobldXF3fddRelUomFhQX6+vo4evQoExMTxVDa66+/nomJCRYXF7nuuus4fvw4k5OTLC0tcfz4cerr64t2tdLv5OTkTr+NkiQ9rW0qAc3MaZYT0H++PeE8dTz66KOrelbGx8f52te+xnXX5SswqAAAIABJREFUXbfqC7a0E6rVVVe2M5PZ2dmivfJ+ZmamuO/t7V21vaura9X+HR0dxf7V+XYr29WELTOLHqfM5Pz588zMzNDU1ERnZycRUSQD1V6ule3MZHJykrq6uiL5WFxcpK6urkhIqwVyqsneeu3u7m5aWlqKa+ju7mZycrLoaevt7S2qrFbb5XK5mKdZ7bWt9qT19fXR1NRUJKB9fX3Mzs4WMfX39xeJc9WhQ4c2/yHuQvX19Zw8efJJ/1BQ/Zyq78Ha9kY+t+r7m5ksLi4+YXtpaan4nbu2vbS0RGY+7o8ekiRpZ2y2Cu4HgLUTaMaBM8CvZebsVgW2242Nja1ql8tl7rvvPoaHh4kIent7uf76650jqh3R3NxMXV1d8WW7paWFiCiGS64cNrmy/WTb1z6/cr+pqalV7epSHrBcRXp8fJz9+/cD8OCDDzI/P18MBf3CF74AfH3o6AMPPEBTUxMHDx5kaWmJ8+fP88gjj9DW1kZdXR0dHR3FsN/6+no6OjqYn59nZmam6KWdmZlhbm6OxsZG9u/fXwyjXTtvsr29nVtvvZXLly8zNzdHR0cHt912WzFv8ujRo9x+++2r5k22tLSsmjdZrdpanTcZEUXl7P7+/iLZ3SvW/l5b+we3J2uvHQb7RO2IeNL2yvOvba+N1d/JkiTtrM0OwX0YGGB5+C3A3wQuAjcBvw78na0LbXerr69f9Zf0wcHBooclMxkZGaGxsZHrrrtup0JUjZRKJaampopCPdXCPe3t7UWvWq3V19dz+PDhYhhuQ0MDBw4cKOJpbm7mwIEDRWLU1tbG/v37izmdHR0dDAwMrJqPVy6XV/USRkQx327fvn00NjYWQ037+vpWzTetJp/VL//VYZPVnq9qz2e192pqaorZ2dmi4ur58+eL5G5paYlPf/rT7Nu3r6jyevfdd3PgwAG6u7uZnZ3lgQce4LrrrqOjo4OFhQXOnz/PM57xjFVzJTc7T/L6669f1V772a6cJwkU76UkSZK+brMJ6O2Z+aIV7Q9ExMcz80UR8bmtDGy3279/P+fPnwco5kCtLBoCy8U6TED3trNnzxY9XZnJxMTEqsRj37599Pb2Fj1rbW1tjI2NFXPc1haT2UoHDhwoKtc2NDRw2223MTMzU1RXvf3225mamioKtdx+++1MTk4WxXnuuOMOJiYmmJ6epq2tja6uLsbGxophuNWlL6pVcNva2oqlMarDXkdGRiiXy0Uho6pq72xEFDdgVbuarM7PzxdDfYFiaY2JiQn27dvH9PR0sSRId3c3pVKp+CxWJomjo6OPK64jSZKk2tpsAjoQEccy81GAiDgGVEs3zm9pZLvcwYMHi+UJYLkK5doej/r6emZmZqivr98zRUj0daVSqUg+q+0LFy5QV1dHZ2dn0VO3f/9+2traiuIsK39ODh8+vK3zA9va2oq5jPD1dSSrOjs7V/XcdXd3r4qvp6enqHwLqyu3Ao+raruykitQ/FGmsbFxVUXenp4e5ufni6GSK1+jWvio+m+mmohW18KsJqvV7Wvb1XM69FKSJGn32WwC+o+Av4yIrwABnAD+fkS0A+/a6uB2u4GBgeILdkdHB5cuXSqem52dZXp6uijO0tPTw4kTJ55WX4Izc1UhlpXLUjxZe2pqqug13K3Je3X5jqqVhXhWDsWdnZ0tegeHhoZoaWkp5kYODg7S19dHfX39ExZNWa+IynZWjt1q1cT00qVLLC0t8exnP5u6ujpGRkbITG677TYARkZGiAi+4Ru+gcXFRUZHR2ltbeWmm24qrr+lpYXW1tYiIW1vb6e5ublIYjs6Oop1T6uqhYwkSZK0s2Kzi3JHRDPwDJYT0C/uxsJDp0+fzjNnztT0NTOTixcvMjIyQl1dHcPDw8WQwaqDBw8+bt7ZXjQ9Pc2jjz5arKvY2dnJzMwM09PTNDc309nZSalUKoZVdnR0MDk5ydzcHK2trSwsLBTrq0YER44cWbUG4m4xMTHBgw8+WLTHx8e5cOECR44coauri8HBQcbGxjh27Bjt7e2cO3eOyclJDh06VCRHQ0NDtLe309bWRnt7Ow0NDcWyER0dHcWyEZn5uMqx1Z7MycnJYj3Fai9rXV0d/f39RVXmp7qlpSUuXLjA2NgYDQ0N7Nu3j9nZWcbHx2lqaqKvr49SqVQMJ963bx+Tk5OUSiVaWlo4fPiww29ViIh7MvP0TschSdLT0dWsyH0KuBloAW6tzO1699aG9dQTERw8eJCDBw8yPz//uN4xWE5QNpOAjo+PF/Pxenp6nhKJRGby0EMPsbCwACz3Cn72s5/l6NGjtLW1USqVuO+++zh+/DgtLS2MjY1x7733cvLkSRobGxkcHOTixYvceOON1NfXF8t3VK+/2hu6uLjI4uJiTXpHFxcXiwqvzc3NRfXVlpYWuru7i+V4urq6mJ+fL4a0trS00NnZWSQ+LS0tTE5OFn+YGBsb4/Lly0Uy+uijjzI2NsbJkycBePjhh5menub48eMAfPnLXy7WO4TlyrF1dXUcPnyYzOT++++npaWFAwcOsLS0xKVLl2hsbCwK+TyV1dXVcd111z3hnOq1w3/XtiVJkrTzNrsMy5uBFwO3AB8EXg78JfC0T0BXqq5nt7Z3ubqQ/UY8/PDDjI6OFu3Ozk5OnTq165PQiYmJIvkEit688fFx2tramJiYKArEVBOyaruvr4/p6WmWlpaYnJwsErOLFy8yOTlJR0cHzc3NNDQ0MD09TWbS1tbGiRMnaGhoYGlpqUhIFxYWyMxrTlBHR0c5e/bsqorHKz/b/v5+Tp48WVTBveOOOxgfH2d6epoTJ07w2GOPrRqGXVdXVySg1Wuu/lxMTEysKrhTfS8XFhZobGxkYmKCpaWlIpZSqQQszyOtJsXz8/OreotHRkb2RAIqSZKkvWGzPaDfBzwX+ExmvjYiDgC/sfVhPbXV19fT39/P0NDQqu2dnZ1cuHCBxsZGent7GR0dLSqS7t+/n6amJmZnZ5mbm1uVfMJyInfp0iXa2tqK5S1mZ2eZnZ2lvb19U8ntdlqbIK+sbrqR+2rCWG2Pj48zPDxcLAcyODjI8PAwN954IxFBqVTiYx/7GAMDA2Rmsd5lNenr7OxkYGCgGLba19e34aGYi4uLq5LPhYUFvvKVr3Dw4MEiOb58+TLd3d2rerZ7e3uL+YnVz3m9yrGNjY2rkvUne2/q6uqKxHe9Qjwrq8lW7aZ5oJIkSdJmE9CZzFyKiHJEdAGXgBu2Ia6nvKNHjxZDTKvFU6rLtgCcOXOG3t7eInE8e/YsLS0t1NXVMTY2xsLCwqplXS5fvszQ0BADAwNEBIuLi6uSj90yT7Kzs5Pm5mbm5uaK9sqCMF1dXQwPDxeVVru6uhgdHS0SzJ6eHiYnJ4thrNXHK3sNy+UyMzMztLW1MTw8zOXLl+no6KClpYWzZ88yOztbDFs9d+4cn//854s1HoeGhjh58uSqAjVXUiqVVvV8Tk1NkZmUSqVVx09MTFzxfBFxxcqxHR0dq+aQVtfWrBYo2rdvH1NTU8Vamr29vZTL5eJz7+npKX626uvrV71PVWuXBpIkSZJ20mYT0DMR0QP8OnAPUAL+asuj2gMigv3797N//34WFxe5//77i+fm5uYYHh5maWmJgwcPUi6XefTRR+np6eHgwYM0NTUxODhIa2srHR0dzMzMMDQ0VPSyjY+Pc/78eU6cOEFLSwuZyblz5+jp6SmSl50SEZw6daoouNPW1sY3f/M3UyqVKJVKdHR08OIXv7hYX7Kvr48TJ04wPj7OzMwM3d3d3HLLLUUV3Pr6+qIgETx+iY2JiYlV2ycnJymXy5TLZRoaGhgaGiqG41Z7B8+fP09jYyMRQVtbG5nJ1NQU9fX1tLa2srS0VAwFXqn6x4K1vc1XO8y3q6uLG264gYsXL7KwsMCNN95IS0sLw8PDlMtlnvGMZ9DY2Fj8rDznOc8hIhgZGQHgjjvuYGlpidHRUerq6nje855HuVwu/ugxMDBg5VdJkiTtKhtOQGP52/vPZ+YY8LaI+BOgKzPvf5JDn/ZmZ2dXJTPVYZezs8sFhKs9a9VEq62tje7u7iJhq95XewWnp6eL41b2eFUrgO605ubmopDOlaydl7i2Xb3W/v5+vvSlLxVDT3t7e1laWiquu9rzV00K6+rqVg1FXVhYWDUMdXZ2locffrh476v7Vc+/dmmT8fFxurq6iAja29vp7Oxc1aPZ2Nh4TcVuVg7Xrdq/f/+q9tqe7bXrhq4tbPVEhXokSZKknbThBDQzMyL+ELiz0j67XUHtNdWhtdWkprW1lYigtbUV+HrvXXVZDVguLNPZ2Ul3dzf79u0rer1g/Z64xcXFYimTakJ26dIlFhYW6O7upr+/f0sLGI2PjzM+Pk5DQwP9/f1MT08XS2L09/dv2ZzU9vZ2Tp06xWOPPcbCwgIHDhzgOc95DpcvX6ZcLvPMZz5zVcXhffv2FT2nQDFntnrtjz322KrYHnzwQdrb2zl06BCZyRe/+EV6enqKJLCrq6tYd7KpqYlbbrmFiYmJYnmP/fv3F0NkJUmSJD2xzX5z/mREfENm3r0t0exR9fX1XHfddTz66KNF+8SJE0Ui1NHRwYEDB4p5kdV9jh8/TlNTE0tLS8WQVFie+zc9PV30EpbLZS5fvlwkXWfPnmViYqJIosbHxymVSpw4ceKarmN6epq6ujpGRkYYHBwstt93331FogZw6dIlnvnMZ27ZEimdnZ3FtVat7IUcGxvj0qVLLC4ucuTIERoaGhgeHiYzufPOOxkfHy/WFi2Xyxw+fBiA+fl5FhYWigR2dna2WHKlqjonc2WPbmtr666YbytJkiQ91Ww2Af0W4A0RcRaYAoLlztFbtzqwvWZgYIDOzk7Gx8dXFeUplUo0NTXR0NDAY489RqlUorm5uZgLCsvDQp/xjGdw8eLFovjOrbfeWlRXnZqaWjXMdHR0lLGxMXp6eopzjIyMcOTIkatKCmdmZvjKV77C3Nwci4uLDA4OcujQIerr61lYWGB4eJiFhYViKGi5XObixYtF4Z/t1tPT87giQCuHxVaXeQGKhB6+vlxOtQdz7X3VbqkwLEmSJD3VbTYBffm2RPE00dLS8rgqpdXqr/DEc/caGhoeN9evOhfwq1/9atE7Cl+fYzo3N1cknJnJpUuXaG5upru7u+g1XVxcZN++fXR3dxdra65dpuTs2bPF+RcWFpicnKShoYGDBw8yPz+/6jWrVsaz0yKi6F0+ePAgFy5cAJYT0N7e3uJ6q38YWNkTXVdXZ2+nJEmStEU2lYBm5iMR8deAU5n5XyJiAOh4suO0vdra2lbNEW1vb2d6erqYY1oulzl37hywnIxNTU2xtLRUDGu9dOkS8/PztLW1AcuJcnWdzXK5XBQ9guUCQ/X19ZRKpWLflfNZq1bOZ91NDh06REtLCyMjI9TV1XHzzTczNzdXVI69+eabmZmZKXqq9+/f/7hrkyRJknR1olr9c0M7R7wZOA3cnJk3RcRh4Hcy8wXbFeDVOH36dJ45c2anw6iZpaUlvvzlLxdzFzOTxcXFYijp0NBQsaQLwCOPPMLc3BynTp0iIjh37hxTU1PcdNNNRdI5NjZGf38/S0tLXLhwoRhyC8vVdkdHRzl27BiwPJeyvr5+VTGlU6dOrRoWLEm7RUTck5mndzoOSZKejjY7BPe7gduBTwNk5oWI6HziQ7Tdqj15ExMTzM3NFQWBZmdnWVhYKB5XLSwssLi4yPz8PM3NzZRKpVVJ6+DgIKVSif7+furq6mhqauLSpUvFkN/Ozs5iTcrGxkY6OzuLoblNTU27tvdTkiRJ0s7abAI6X1mOJQEiov3JDlBtrJznWFWdc9rW1rYqAW1vb2diYmLVci7VYjyZSalUWlWIZ//+/ZRKJbq6uqivry8KKq3U2Ni4qjKtJEmSJK212QT0fRHxa0BPRPwo8CPAr299WNpKhw4dYmJignK5DCxX5O3u7i6GyB48eLAYPhsR1NXVraoiC8tVZU+dOlXbwCVJkiTtKZstQvRLEfESYAK4GfjZzPzIkx0XEe8EXglcysxnV7btA34bOA6cBf5GZo6uc+xrgH9Raf7fmfmuzcSs5Z7QW265hZGREcrlMj09PbS3t1MqlYpiRJOTk1y+fBmA22+/fVXhIaBYU1SSJEmSrtZmixD9Q5aLDp3b1ItEvAgoAe9ekYD+IjCSmW+NiDcBvZn5M2uO2wecYbnwUQL3AHeul6iu9HQrQrQdhoaGGB4eJiIYGBhweK2kPcMiRJIk7ZzNDsHtAj4UESPAe4HfzcyLT3ZQZn48Io6v2fwq4MWVx+8CPgr8zJp9vh34SGaOAETER4CXAe/ZZNzapIGBAQYGBnY6DEmSJEl7yKbWycjMf5mZzwLeCBwGPhYR/+sqX/tAZg5WzjsIrDfG8wjwtRXtc5VtkiRJkqSnmKtdqPES8BgwzPqJ41aJdbatO2Y4Il4fEWci4szQ0NA2hiRJkiRJuhqbSkAj4sci4qPAnwL9wI9m5q1X+doXI+JQ5byHWE5q1zoHHF3Rvg64sN7JMvPtmXk6M087dFSSJEmSdp/N9oBeD/xUZj4rM9+cmZ+/htd+P/CayuPXAP9jnX0+BLw0Inojohd4aWWbJEmSJOkpZrNzQN+UmfcCRER7RPxgRPzRkx0XEe8BPgHcHBHnIuJ1wFuBl0TEg8BLKm0i4nRE/Ebl9UaAfwXcXbm9pVqQSJIkSZL01LLZZViagFcAf4vlarS/B/x+Zn5ge8K7Oi7DIkm6EpdhkSRp52xoGZaIeAnwAywvi/LnwH8FnpeZr93G2CRJkiRJe8hG1wH9EPAXwF/LzK8CRMSvbFtUkiRJkqQ9Z6MJ6J3Aq4H/FREPA+8F6rctKkmSJEnSnrOhIkSZ+ZnM/JnMPAn8HHA70BQRfxwRr9/OACVJkiRJe8Nml2EhM/93Zv44cAT4d8BdWx6VJEmSJGnP2egQ3MfJzCWW54a6LqckSZIk6UltugdUkiRJkqSrYQIqSZIkSaqJTSWgEfFLEfGs7QpGkiRJkrR3bbYH9IvA2yPiUxHxhojo3o6gJEmSJEl7z6YS0Mz8jcx8AfBDwHHg/oj4rYj4lu0ITpIkSZK0d2x6DmhE1APPqNwuA/cBPx0R793i2CRJkiRJe8imlmGJiF8GvgP4M+BfZ+ZfVZ76hYj40lYHJ0mSJEnaOza7DugDwL/IzOl1nnveFsQjSZIkSdqjNjsE92+vTT4j4k8BMnN8y6KSJEmSJO05G+oBjYgWoA3oj4heICpPdQGHtyk2SZIkSdIestEhuH8P+CmWk81Pr9g+AfzHrQ5KkiRJkrT3bCgBzcxfAX4lIv5BZv6HbY5JkiRJkrQHbXQI7l/PzD8DzkfE96x9PjN/f8sjkyRJkiTtKRsdgvvNLC+98h3rPJeACagkSZIk6QltdAjumyv3r93ecCRJkiRJe9WmlmGJiJ+MiK5Y9hsR8emIeOl2BSdJkiRJ2js2uw7oj2TmBPBSYD/wWuCtWx6VJEmSJGnP2WwCWl3/8xXAf8nM+1ZskyRJkiTpijabgN4TER9mOQH9UER0AktbH5YkSZIkaa/ZaBXcqtcBtwEPZ+Z0RPSxPAxXkiRJkqQntKkENDOXIuI64G9FBMDHMvMD2xKZJEmSJGlP2WwV3LcCPwl8vnL7iYj4+e0ITJIkSZK0t2x2DugrgJdk5jsz853Ay4D/41oCqCzt8kBEfC4ifmqd518cEeMRcW/l9rPX8nqSJEmSpJ2x2TmgAD3ASOVx97W8eEQ8G/hR4HnAPPAnEfFHmfngml3/IjNfeS2vJUmSJEnaWZtNQH8e+ExE/DnLy6+8CPin1/D6zwQ+mZnTABHxMeC7gV+8hnNKkiRJknahDQ/BjeWqQ38JPB/4/crtrsx87zW8/gPAiyKiLyLaWB7ie3Sd/e6KiPsi4o8j4lnX8HqSJEmSpB2y4R7QzMyI+MPMvBN4/1a8eGZ+ISJ+AfgIUALuA8prdvs0cH1mliLiFcAfAqfWnisiXg+8HuDYsWNbEZ4kSZIkaQtttgjRJyPiG7YygMx8R2bekZkvYnlu6YNrnp/IzFLl8QeBxojoX+c8b8/M05l5emBgYCtDlCRJkiRtgc3OAf0W4O9FxCPAFMvzQDMzb73aACJif2ZeiohjwPcAd615/iBwsdID+zyWk+bhq309SZIkSdLO2GwC+vJtiOH3IqIPWADemJmjEfEGgMx8G/B9wI9FRBmYAV6dmbkNcUiSJEmSttGmEtDMfCQingu8sLLpLzLzvmsJIDNfuM62t614/KvAr17La0iSJEmSdt6m5oBGxE8C/x3YX7n9t4j4B9sRmCRJkiRpb9nsENzXAd+YmVMAlQq2nwD+w1YHJkmSJEnaWzZbBTeAxRXtxco2SZIkSZKe0GZ7QP8L8KmI+INK+7uAd2xtSJIkSZKkvWizRYh+OSI+Cvw1lns+X5uZn9mOwCRJkiRJe8uGEtCIaAHeANwIfBb4T5lZ3s7AJEmSJEl7y0bngL4LOM1y8vly4Je2LSJJkiRJ0p600SG4t2TmcwAi4h3AX21fSJIkSZKkvWijPaAL1QcOvZUkSZIkXY2N9oA+NyImKo8DaK20A8jM7NqW6CRJkiRJe8aGEtDMrN/uQCRJkiRJe9tGh+BKkiRJknRNTEAlSZIkSTVhAipJkiRJqgkTUEmSJElSTZiASpIkSZJqwgRUkiRJklQTJqCSJEmSpJowAZUkSZIk1YQJqCRJkiSpJkxAJUmSJEk1YQIqSZIkSaoJE1BJkiRJUk2YgEqSJEmSasIEVJIkSZJUEyagkiRJkqSaMAGVJEmSJNWECagkSZIkqSZ2PAGNiJ+MiAci4nMR8VPrPB8R8e8j4qGIuD8i7tiJOCVJkiRJ12ZHE9CIeDbwo8DzgOcCr4yIU2t2ezlwqnJ7PfCfaxqkJEmSJGlL7HQP6DOBT2bmdGaWgY8B371mn1cB785lnwR6IuJQrQOVJEmSJF2bnU5AHwBeFBF9EdEGvAI4umafI8DXVrTPVbZJkiRJkp5CGnbyxTPzCxHxC8BHgBJwH1Bes1usd+jaDRHxepaH6HLs2LEtjlSSJEmSdK12ugeUzHxHZt6RmS8CRoAH1+xyjtW9otcBF9Y5z9sz83Rmnh4YGNi+gCVJkiRJV2XHE9CI2F+5PwZ8D/CeNbu8H/ihSjXc5wPjmTlY4zAlSZIkSddoR4fgVvxeRPQBC8AbM3M0It4AkJlvAz7I8tzQh4Bp4LU7FqkkSZIk6arteAKamS9cZ9vbVjxO4I01DUqSJEmStOV2fAiuJEmSJOnpwQRUkiRJklQTJqCSJEmSpJowAZUkSZIk1YQJqCRJkiSpJkxAJUmSJEk1YQIqSZIkSaoJE1BJkiRJUk2YgEqSJEmSasIEVJIkSZJUEyagkiRJkqSaMAGVJEmSJNWECagkSZIkqSZMQCVJkiRJNWECKkmSJEmqCRNQSZIkSVJNmIBKkiRJkmrCBFSSJEmSVBMmoJIkSZKkmjABlSRJkiTVhAmoJEmSJKkmTEAlSZIkSTVhAipJkiRJqgkTUEmSJElSTZiASpIkSZJqwgRUkiRJklQTJqCSJEmSpJowAZUkSZIk1cSOJ6AR8Q8j4nMR8UBEvCciWtY8/8MRMRQR91Zuf3enYpUkSZIkXb0dTUAj4gjwE8DpzHw2UA+8ep1dfzszb6vcfqOmQUqSJEmStsSO94ACDUBrRDQAbcCFHY5HkiRJkrQNdjQBzczzwC8BjwKDwHhmfnidXb83Iu6PiN+NiKM1DVKSJEmStCV2eghuL/Aq4ARwGGiPiB9cs9sHgOOZeSvwv4B3XeFcr4+IMxFxZmhoaDvDliRJkiRdhZ0egvttwFczcygzF4DfB75p5Q6ZOZyZc5XmrwN3rneizHx7Zp7OzNMDAwPbGrQkSZIkafN2OgF9FHh+RLRFRADfCnxh5Q4RcWhF8zvXPi9JkiRJempo2MkXz8xPRcTvAp8GysBngLdHxFuAM5n5fuAnIuI7K8+PAD+8U/FKkiRJkq5eZOZOx7DlTp8+nWfOnNnpMCRJu1BE3JOZp3c6DkmSno52egiuJEmSJOlpwgRUkiRJklQTJqCSJEmSpJowAZUkSZIk1YQJqCRJkiSpJkxAJUmSJEk1YQIqSZIkSaoJE1BJkiRJUk2YgEqSJEmSasIEVJIkSZJUEyagkiRJkqSaMAGVJEmSJNWECagkSZIkqSZMQCVJkiRJNWECKkmSJEmqCRNQSZIkSVJNmIBKkiRJkmrCBFSSJEmSVBMmoJIkSZKkmjABlSRJkiTVhAmoJEmSJKkmTEAlSZIkSTVhAipJkiRJqgkTUEmSJElSTZiASpIkSZJqwgRUkiRJklQTJqCSJEmSpJowAZUkSZIk1cSOJ6AR8Q8j4nMR8UBEvCciWtY83xwRvx0RD0XEpyLi+M5EKkmSJEm6FjuagEbEEeAngNOZ+WygHnj1mt1eB4xm5o3A/wv8Qm2jlCRJkiRthR3vAQUagNaIaADagAtrnn8V8K7K498FvjUioobxSZIkSZK2wI4moJl5Hvgl4FFgEBjPzA+v2e0I8LXK/mVgHOirZZySJEmSpGvXsJMvHhG9LPdwngDGgN+JiB/MzP+2crd1Ds11zvV64PWV5lxEPLDV8dZYP3B5p4PYAnvhOryG3cFr2B32wjXcvNMBSJL0dLWjCSjwbcBXM3MIICJ+H/gmYGUCeg44CpyrDNPtBkbWnigz3w68vXKeM5l5eptj31Z74Rpgb1yH17A7eA27w165hp2OQZKkp6udngP6KPD8iGirzOv8VuALa/Z5P/CayuPvA/4sMx/XAypJkiRJ2t12eg7op1guLPRp4LOVeN4eEW+JiO+s7PYOoC8iHgJ+GnjTjgQrSZIkSbomOz0El8x8M/DmNZt/dsXzs8D3b/K0b7+MB9yzAAAJHElEQVTWuHaBvXANsDeuw2vYHbyG3cFrkCRJVy0czSpJkiRJqoWdngMqSZIkSXqa2HMJaES8LCK+FBEPRcSunS8aEUcj4s8j4gsR8bmI+MnK9p+LiPMRcW/l9ooVx/zTynV9KSK+feei/7qIOBsRn63EeqaybV9EfCQiHqzc91a2R0T8+8o13B8Rd+xs9BARN694r++NiImI+Knd/jlExDsj4tLK5Yau5n2PiNdU9n8wIl6z3mvV+Br+TUR8sRLnH0RET2X78YiYWfF5vG3FMXdWfgYfqlzneks31fIaNv2zs5O/t65wDb+9Iv6zEXFvZftu/Ryu9Pv0KfVvQpKkp4XM3DM3oB74CnAD0ATcB9yy03FdIdZDwB2Vx53Al4FbgJ8D/vE6+99SuZ5mltdN/QpQvwuu4yzQv2bbLwJvqjx+E/ALlcevAP6Y5bVdnw98aqfjX+fn5zHg+t3+OQAvAu4AHrja9x3YBzxcue+tPO7d4Wt4KdBQefwLK67h+Mr91pznr4C7Ktf3x8DLd/gaNvWzs9O/t9a7hjXP/1vgZ3f553Cl36dPqX8T3rx58+bN29Phttd6QJ8HPJSZD2fmPPBe4FU7HNO6MnMwMz9deTzJ8vIzR57gkFcB783Mucz8KvAQy9e7G70KeFfl8buA71qx/d257JNAT0Qc2okAr+Bbga9k5iNPsM+u+Bwy8+M8fj3czb7v3w58JDNHMnMU+Ajwsu2Pftl615CZH87McqX5SeC6JzpH5Tq6MvMTmZnAu/n6dW+7K3wOV3Kln50d/b31RNdQ6cX8G8B7nugcu+BzuNLv06fUvwlJkp4O9loCegT42or2OZ44qdsVIuI4cDvwqcqmH68MC3tndcgYu/faEvhwRNwTEa+vbDuQmYOw/MUQ2F/ZvluvoerVrP6i/VT6HGDz7/tuvhaAH2G5l6rqRER8JiI+FhEvrGw7wnLcVbvlGjbzs7ObP4cXAhcz88EV23b157Dm9+le+zchSdJT3l5LQNebc7Sry/xGRAfwe8BPZeYE8J+Bk8BtwCDLw99g917bCzLzDuDlwBsj4kVPsO9uvQYiogn4TuB3Kpueap/DE7lSzLv2WiLinwNl4L9XNg0CxzLzdpbXA/6tiOhid17DZn92duM1VP0Aq/8os6s/h3V+n15x13W27fbPQpKkPWGvJaDngKMr2tcBF3YolicVEY0sf1n675n5+wCZeTEzFzNzCfh1vj68c1deW2ZeqNxfAv6A5XgvVofWVu4vVXbflddQ8XLg05l5EZ56n0PFZt/3XXktlcIvrwT+dmU4J5Vhq8OVx/ewPGfyJpavYeUw3R2/hqv42dmtn0MD8D3Ab1e37ebPYb3fp+yRfxOSJO0ley0BvRs4FREnKj1arwbev8Mxrasyt+odwBcy85dXbF85J/K7gWplyvcDr46I5og4AZxiuejHjomI9ojorD5muYDMAyzHWq0e+Rrgf1Qevx/4oUoFyucD49XhcbvAqp6ep9LnsMJm3/cPAS+NiN7KMNGXVrbtmIh4GfAzwHdm5vSK7QMRUV95fAPL7/vDleuYjIjnV/5N/RBfv+4dcRU/O7v199a3AV/MzGJo7W79HK70+5Q98G9CkqS9pmGnA9hKmVmOiB9n+QtDPfDOzPzcDod1JS8A/g7w2agscQD8M+AHIuI2lod9nQX+HkBmfi4i3gd8nuWhiW/MzMWaR73aAeAPlr/70QD8Vmb+SUTcDbwvIl4HPAp8f2X/D7JcffIhYBp4be1DfryIaANeQuW9rvjF3fw5RMR7gBcD/RFxDngz8FY28b5n5khE/CuWEyCAt2TmRgvqbNc1/FOWq8R+pPJz9cnMfAPLlVrfEhFlYBF4w4pYfwz4TaCV5TmjK+eN7sQ1vHizPzs7+XtrvWvIzHfw+DnRsEs/B678+/Qp9W9CkqSng6iMcJMkSZIkaVvttSG4kiRJkqRdygRUkiRJklQTJqCSJEmSpJowAZUkSZIk1YQJqCRJkiSpJkxAJUmSJEk1YQIq1UhEHIyI90bEVyLi8xHxwYi46UmOKVXuj0fEA+s8XxcR/z4iHoiIz0bE3RFxovLcByOiZwvi/ncR8aJN7D8QEX9yra8rSZKkvccEVKqBiAjgD4CPZubJzLwF+GfAgWs89d8EDgO3ZuZzgO8GxgAy8xWZOXYtJ4+IfcDzM/PjGz0mM4eAwYh4wbW8tiRJkvYeE1CpNr4FWMjMt1U3ZOa9mfkXABHxf1Z6L++PiH+5ifMeAgYzc6lyznOZOVo559mI6I+IN0TEvZXbVyPizyvPvzQiPhERn46I34mIjnXO/31A0ZtZOee/rhx3JiLuiIgPVXp137DiuD8E/vYmrkOSJElPAyagUm08G7hnvSci4qXAKeB5wG3AnZsY8vo+4DsqyeW/jYjb1+6QmW/LzNuAbwDOAb8cEf3AvwC+LTPvAM4AP73O+V+wTtxfy8y7gL8AfpPlJPX5wFtW7HMGeOEGr0GSJElPEw07HYAkXlq5fabS7mA5IX3SYa+ZeS4ibgb+euX2pxHx/Zn5p+vs/ivAn2XmByLilcAtwP9eHh1ME/CJdY45BAyt2fb+yv1ngY7MnAQmI2I2Inoqw34vsTw0WJIkSSqYgEq18TmWewrXE8DPZ+avXc2JM3MO+GPgjyPiIvBdwKoENCJ+GLge+PEVr/mRzPyBJzn9DNCyZttc5X5pxeNqu/o7paVyrCRJklRwCK5UG38GNEfEj1Y3RMQ3RMQ3Ax8CfqQ6BzMijkTE/o2ctDIH83DlcR1wK/DImn3uBP4x8IPVuaLAJ4EXRMSNlX3arlCR9wvAjZu4zqqbgMdV7ZUkSdLTmwmoVAOZmSxXqH1JpWDP54CfAy5k5oeB3wI+ERGfBX4X6NzgqfcDH6gs0XI/UAZ+dc0+Pw7sA/68Mlf0NyqVan8YeE9E3M9yQvqMdc7/R8CLN3yhX/ctlWMlSZKkQix/L5ak9UXEXwKv3MySLhHxceBV1Yq8kiRJEpiASnoSEfGNwExm3r/B/QeAF2TmH25vZJIkSXqqMQGVJEmSJNWEc0AlSZIkSTVhAipJkiRJqgkTUEmSJElSTZiASpIkSZJqwgRUkiRJklQT/z+NKkH8hufkdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats\n",
    "\n",
    "samples_cluster['wts'] = wts            # add the weights to the sample data\n",
    "samples_cluster.head()\n",
    "\n",
    "plt.subplot(321)\n",
    "locmap_st(samples_cluster,'X','Y','wts',xmin,xmax,ymin,ymax,0.0,2.0,'Declustering Weights','X (m)','Y (m)','Weights',cmap)\n",
    "\n",
    "plt.subplot(322)\n",
    "hist_st(samples_cluster['wts'],0.0,2.0,log=False,cumul=False,bins=20,weights=None,xlabel=\"Weights\",title=\"Declustering Weights\")\n",
    "plt.ylim(0.0,20)\n",
    "\n",
    "plt.subplot(323)\n",
    "hist_st(samples_cluster['Realization'],0.0,20.0,log=False,cumul=False,bins=20,weights=None,xlabel=\"Porosity\",title=\"Naive Porosity\")\n",
    "plt.ylim(0.0,20)\n",
    "\n",
    "plt.subplot(324)\n",
    "hist_st(samples_cluster['Realization'],0.0,20.0,log=False,cumul=False,bins=20,weights=samples_cluster['wts'],xlabel=\"Porosity\",title=\"Declustered Porosity\")\n",
    "plt.ylim(0.0,20)\n",
    "\n",
    "plt.subplot(325)\n",
    "plt.scatter(cell_sizes,averages, c = \"black\", marker='o', alpha = 0.2, edgecolors = \"none\")\n",
    "plt.xlabel('Cell Size (m)')\n",
    "plt.ylabel('Porosity Average (%)')\n",
    "plt.title('Porosity Average vs. Cell Size')\n",
    "plt.ylim(8,12)\n",
    "plt.xlim(0,2000)\n",
    "\n",
    "print(scipy.stats.describe(wts))\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=3.5, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "There are so many more exercised and tests that one could attempt to gain experience with decison trees. I'll end here for brevity, but I invite you to continue. Consider, on your own apply other data sets or attempting modeling with random forest and boosting.  I hope you found this tutorial useful. I'm always happy to discuss geostatistics, statistical modeling, uncertainty modeling and machine learning,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "**Michael Pyrcz**, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "On Twitter I'm the **GeostatsGuy** and on YouTube my lectures are on the channel, **GeostatsGuy Lectures**."
   ]
  }
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